Introduction

Raw data

The raw data is shown below. There were 214 rows and 80 columns, consisting of 32 patients sampled over 7 timepoints. However, there is a significant amount of missing data, resulting in only 166 usable datapoints.

missing.ix <- Reduce(intersect, apply(df[,bcellcyto], 2, function(x) which(is.na(x))))
df.raw <- df[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw, 
      digits = 3,
      row.names = T,
      caption = "Raw Data"
) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%", height = "300px") 
Raw Data
PatientID Time Age AgeGreater60 Sex LowIntermacs InterMACS RVAD Sensitized VAD Indication Device Type Outcome Survival num Total PBMC num lymph lymph live lymph CD3 of live lymph CD19 of live lymph CD19+CD27- CD19+CD27+ CD27+38++plasma blasts CD27-38++ transitional CD27-IgD+ mature naive CD27+IgD- switched memory CD27-IgD- switched memory CD27+IgD+ unswitched memory CD27+IgD-IgM+ switched memory CD27+IgD+IgM+ nonswitched memory CD19+27+IgG+IgM- memory CD19+24dim38dim naive mature CD19+24+38++transitional CD19CD24hiCD38-memory CD19+27-38+CD5+transitionals CD19+CD268+ CD268 of +27-38++transitional CD19+CD11b+ CD19+CD5+ CD19+CD27+CD24hi CD19+CD5+CD24hi CD19+CD5+CD11b+ CD19+27+IgD-38++IgG ASC IL-12(p40) IL-12(p70) IFN-g TNF-a TNF-b IL-4 IL-5 IL-9 IL-10 IL-13 IL-17A IL-1a IL-1b IL-2 IL-3 IL-6 IL-15 TGF-a IFN-a2 IL-8 GRO Eotaxin MDC IP-10 MCP-1 MCP-3 Fractalkine MIP-1a MIP-1b GM-CSF IL-7 G-CSF VEGF EGF FGF-2 Flt-3L IL-1RA sCD40L
1 1 0 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 169154 35496 20.98 99.53 25.37 19.82 86.02 13.98 1.26 2.54 57.81 11.74 28.06 2.39 21.74 14.36 0.72 81.05 2.86 13.60 0.58 95.22 84.83 10.47 4.33 8.50 1.39 3.13 1.09 1.71 2.16 124.000 20.926 2.72 2.500 1.010 7.273 4.617 1.760 21.20 277.000 3.483 9.878 1.160 12.334 1.090 5.71 2.18 45.38 126.00 119.000 525.00 1174 392 3.03 27.25 2.030 47.654 60.843 1.268 33.267 486.000 54.27 6.05 1.92 4.47 793.00
2 1 1 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 63082 9915 15.72 99.26 32.45 26.26 90.06 9.94 0.89 2.09 54.27 7.70 35.71 2.32 31.10 20.47 1.57 84.06 2.59 10.79 0.26 97.02 92.59 7.50 3.13 6.42 1.32 2.44 1.49 1.71 1.71 156.000 24.857 2.72 2.500 1.010 1.270 48.726 1.760 29.97 388.000 1.350 1.952 1.160 109.000 5.715 3.89 3.18 102.00 191.00 95.541 463.00 1079 552 3.03 35.53 2.030 62.089 12.293 2.005 109.000 509.000 64.48 26.13 1.92 19.50 841.00
3 1 3 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 75921 21721 28.61 99.31 24.86 33.01 86.38 13.62 1.54 1.26 45.74 10.98 40.44 2.84 31.80 18.04 1.15 86.21 4.82 6.56 0.24 90.41 73.33 10.00 7.25 10.12 3.22 5.67 2.05 32.32 14.40 259.000 28.330 14.74 12.770 3.369 5.615 28.055 7.406 54.61 438.000 3.867 8.638 3.690 57.616 12.778 5.85 56.84 121.00 193.00 186.000 510.00 1365 464 35.08 152.00 2.030 87.775 44.032 10.100 81.038 687.000 70.06 117.00 1.92 151.00 801.00
4 1 5 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.71 5.07 134.000 41.230 2.72 2.813 1.809 4.930 23.012 1.760 26.38 316.000 1.219 1.230 2.735 15.579 5.021 2.73 25.80 78.31 231.00 213.000 462.00 2053 583 4.14 104.00 2.030 55.375 49.222 6.820 29.004 414.000 61.03 43.25 1.92 64.51 1230.00
5 1 8 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 213808 36002 16.84 99.19 35.95 14.55 67.94 32.06 4.06 2.17 27.92 28.92 39.64 3.52 20.31 9.10 1.33 68.69 7.10 20.42 1.88 87.38 44.25 10.78 11.12 19.15 5.54 7.35 1.07 1.71 3.83 228.000 23.577 2.81 2.500 1.666 6.792 39.103 1.760 43.65 450.000 2.412 6.904 2.197 19.163 2.445 3.75 19.72 56.79 134.00 184.000 496.00 1214 430 3.03 90.07 2.030 72.549 44.681 4.448 51.520 735.000 78.74 94.11 1.92 133.00 912.00
6 1 21 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 106970 31867 29.79 99.18 42.36 15.02 84.69 15.31 1.94 2.11 55.41 13.14 29.13 2.32 20.22 12.88 2.08 83.21 5.20 9.50 1.10 94.44 73.00 9.04 6.99 9.22 2.80 4.78 1.44 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
7 2 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 47.38 62.54 800.000 44.293 47.38 58.846 142.000 8.004 27.135 23.250 152.00 1482.000 15.724 10.414 10.464 169.000 15.490 9.98 136.00 144.00 414.00 217.000 834.00 2701 464 74.28 328.00 44.782 220.000 47.925 53.810 185.000 1845.000 220.00 275.00 661.00 251.00 1300.00
8 2 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.71 108.00 305.000 26.686 2.72 2.500 24.287 1.270 40.482 1.760 92.32 396.000 0.858 1.230 1.228 177.000 13.937 8.89 16.74 122.00 86.72 106.000 470.00 1039 267 10.10 84.06 17.074 190.000 11.123 3.608 17.674 1146.000 145.00 311.00 365.00 583.00 1584.00
9 2 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4.78 65.82 441.000 41.771 8.78 13.363 43.505 3.412 20.281 4.002 99.92 620.000 2.755 2.491 5.542 132.000 17.441 9.57 66.07 124.00 334.00 227.000 441.00 681 419 24.50 182.00 19.737 214.000 19.603 12.708 78.434 1314.000 146.00 311.00 439.00 251.00 1349.00
10 2 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 16.71 59.25 490.000 44.652 16.00 36.320 46.100 5.500 18.023 9.218 109.00 700.000 4.262 3.356 7.955 133.000 16.270 9.71 90.20 136.00 386.00 245.000 434.00 899 587 27.78 205.00 22.552 205.000 26.568 16.970 101.000 1464.000 153.00 361.00 469.00 205.00 1335.00
11 2 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.71 114.00 563.000 71.393 2.81 25.856 61.646 2.705 6.884 1.760 143.00 749.000 2.086 4.590 2.553 148.000 9.354 10.52 41.30 140.00 249.00 258.000 479.00 1372 764 26.17 223.00 34.562 258.000 25.928 6.167 59.758 1701.000 159.00 489.00 525.00 263.00 801.00
12 3 0 81 older Male Low 3 No NA DT HMII Died dead 119377 77509 64.93 99.61 58.50 7.99 62.67 37.33 9.10 4.82 36.73 31.90 25.62 5.74 11.47 8.95 0.40 69.54 7.69 9.41 2.61 76.44 57.91 23.29 20.76 15.39 5.42 14.90 2.60 6.39 6.65 11.372 21.497 3.51 2.320 6.303 1.590 6.039 2.111 5.18 4.727 1.920 1.450 2.558 38.074 5.729 2.57 19.65 32.02 123.00 133.000 380.00 1899 1054 26.14 112.00 2.230 32.026 20.483 7.783 58.902 70.513 7.02 95.30 0.39 89.23 624.00
13 3 1 81 older Male Low 3 No NA DT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 41.70 5.35 39.418 40.838 13.66 17.416 2.457 4.050 57.842 9.006 7.14 46.122 4.879 6.666 3.299 119.000 26.234 2.86 67.14 112.00 206.00 180.000 411.00 1271 4441 35.46 169.00 33.419 100.000 54.262 5.253 43.646 118.000 24.49 79.63 0.39 224.00 1714.00
14 3 3 81 older Male Low 3 No NA DT HMII Died dead 96940 57658 59.48 99.70 55.52 9.21 72.15 27.85 3.85 3.82 48.41 21.75 23.49 6.35 12.10 16.04 0.28 75.30 3.72 6.65 2.11 80.52 67.33 15.80 15.23 6.71 1.32 8.86 0.26 17.15 6.00 20.672 15.342 7.00 5.509 7.681 2.040 8.932 3.799 4.45 17.129 2.699 3.725 3.108 25.794 15.326 2.57 74.20 34.62 85.04 162.000 348.00 897 1361 21.65 102.00 6.821 27.057 24.672 5.253 34.257 70.513 13.56 63.96 0.39 118.00 1014.00
15 3 5 81 older Male Low 3 No NA DT HMII Died dead 154373 73969 47.92 99.65 45.18 8.77 67.36 32.64 6.59 2.77 39.10 26.28 27.95 6.67 17.89 15.71 0.39 71.57 6.26 9.88 1.78 81.86 72.07 20.12 20.17 14.80 5.99 14.65 3.71 4.28 2.29 12.657 16.490 2.31 2.320 6.816 1.590 6.845 1.520 4.74 2.120 1.920 1.450 1.980 99.474 11.334 1.58 47.29 61.82 48.64 192.000 320.00 738 1071 10.05 46.41 2.230 21.777 11.020 1.520 37.403 27.750 2.89 23.91 0.39 52.69 361.00
16 3 8 81 older Male Low 3 No NA DT HMII Died dead 155610 63375 40.73 99.51 38.42 12.40 71.69 28.31 4.36 2.62 37.82 23.43 33.50 5.24 17.37 14.92 0.60 65.04 2.80 11.23 2.54 83.55 37.56 15.55 15.45 9.55 2.66 10.91 0.28 8.54 4.12 17.955 15.342 3.51 2.320 6.816 1.590 6.439 1.605 5.62 13.939 2.314 1.782 2.042 79.869 8.070 2.42 84.63 34.26 38.88 162.000 335.00 822 1054 16.46 84.38 2.980 19.937 20.483 2.734 21.566 90.169 10.50 41.25 0.39 77.29 562.00
17 3 14 81 older Male Low 3 No NA DT HMII Died dead 244245 115109 47.13 99.47 47.40 9.37 67.50 32.50 3.99 2.08 50.08 26.01 17.22 6.68 15.34 15.25 0.23 77.86 3.21 10.49 1.48 82.13 59.19 21.45 19.77 14.45 2.57 14.94 0.94 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
18 4 1 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 246811 86529 35.06 93.49 67.96 17.48 81.66 18.34 2.05 6.85 51.33 13.69 29.91 5.08 15.68 5.94 0.19 61.95 0.76 13.51 4.07 92.62 34.06 6.90 12.07 8.00 2.54 3.85 0.26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
19 4 3 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 406771 90343 22.21 97.34 49.28 17.02 75.04 24.96 4.47 18.36 28.45 21.86 46.07 3.62 24.97 4.33 0.13 47.41 1.64 19.70 18.15 72.76 8.08 25.69 28.35 13.95 4.66 19.20 0.41 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
20 4 21 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 330191 81636 24.72 94.57 45.14 14.70 70.51 29.49 8.94 16.23 38.92 23.53 30.83 6.72 23.93 7.07 0.18 46.63 2.35 16.12 8.59 74.45 39.90 24.19 21.18 12.62 3.26 12.93 0.38 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
21 5 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 41.24 109.00 187.000 25.955 53.99 42.552 10.747 12.567 51.239 17.031 68.25 64.203 7.364 13.383 10.807 70.212 17.104 10.88 161.00 95.72 536.00 281.000 2294.00 624 929 24.90 228.00 22.644 85.566 58.104 17.699 193.000 1151.000 107.00 183.00 41.52 377.00 6509.00
22 5 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 25.64 18.30 67.494 20.261 8.29 11.301 2.861 4.489 59.406 3.964 20.30 20.414 1.860 4.133 3.291 211.000 13.360 3.81 94.97 78.63 197.00 81.039 880.00 512 1398 16.57 80.75 13.454 68.452 36.398 4.775 71.797 376.000 23.81 173.00 19.21 128.00 2712.00
23 5 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 65.91 44.73 94.077 18.237 66.52 41.525 10.572 11.269 39.553 17.428 29.48 72.616 8.804 10.630 17.422 203.000 21.250 8.49 159.00 54.16 674.00 230.000 562.00 201 607 37.07 181.00 13.404 67.666 56.614 14.252 152.000 646.000 78.65 138.00 26.01 319.00 10443.75
24 5 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 45.56 41.78 173.000 17.642 45.32 43.807 7.681 8.007 29.730 15.356 50.08 131.000 8.403 10.203 12.135 96.449 20.663 8.39 173.00 74.75 989.00 349.000 615.00 223 507 35.46 195.00 16.773 97.826 50.067 27.582 147.000 859.000 103.00 301.00 12.72 199.00 9557.42
25 5 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 46.23 76.19 129.000 19.211 64.75 40.499 12.238 12.567 41.854 15.844 57.83 87.429 9.848 14.449 16.809 92.658 17.104 9.86 177.00 68.47 747.00 184.000 709.00 491 642 34.12 181.00 16.173 77.694 62.554 17.001 163.000 790.000 101.00 212.00 30.50 325.00 10443.75
26 6 0 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive 309890 41896 13.52 97.03 47.13 8.08 77.31 22.69 2.74 3.29 59.03 19.80 19.37 1.80 21.10 4.46 19.79 71.98 2.41 17.39 0.20 95.80 75.00 5.79 1.89 16.45 0.91 0.67 0.77 4.28 4.73 52.585 19.181 5.78 2.320 1.758 1.590 16.952 1.605 8.23 2.120 1.920 1.450 2.382 38.750 1.342 12.51 27.01 37.91 534.00 147.000 353.00 425 293 21.65 77.95 6.139 44.075 11.020 5.888 46.738 153.000 45.46 128.00 0.39 129.00 2818.00
27 6 1 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive 274228 15653 5.71 97.85 35.76 28.87 51.45 48.55 2.60 2.13 30.85 43.13 22.77 3.26 20.09 3.92 30.51 57.58 1.31 28.20 0.18 91.23 59.57 12.08 2.04 34.44 0.38 0.16 0.16 2.18 4.12 60.794 37.589 2.31 2.320 1.125 1.590 80.735 1.520 12.24 2.120 1.920 1.450 1.980 173.000 3.201 15.43 16.01 121.00 231.00 210.000 265.00 359 515 3.98 77.95 48.595 128.000 6.036 3.987 52.863 60.307 7.02 55.00 0.39 65.11 1125.00
28 6 3 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive 591939 57589 9.73 96.76 38.20 42.54 75.71 24.29 2.14 1.33 53.03 21.26 24.16 1.55 25.50 4.02 20.27 67.18 1.14 21.71 0.11 95.28 66.98 3.79 2.43 17.70 0.65 0.84 0.18 2.18 5.35 60.114 36.632 2.49 2.320 1.496 1.590 48.963 1.520 11.92 2.120 1.920 1.450 2.210 171.000 3.894 13.78 38.10 123.00 342.00 282.000 313.00 467 727 10.05 96.17 8.166 75.587 16.356 5.888 122.000 99.662 15.43 66.76 0.39 124.00 1448.00
29 6 5 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive 189634 46711 24.63 97.74 34.84 37.32 75.30 24.70 0.70 0.55 46.97 22.05 30.28 0.70 23.67 1.56 31.44 73.33 0.75 18.59 0.00 84.61 86.17 3.44 2.11 18.68 0.94 0.63 0.69 2.22 8.01 26.182 21.497 5.78 4.359 2.457 1.686 6.039 3.799 5.33 2.120 1.920 1.450 3.299 16.068 1.623 2.42 41.79 27.18 322.00 227.000 215.00 723 377 13.47 102.00 2.713 38.246 13.657 7.783 43.646 118.000 22.35 66.76 0.39 83.29 2184.00
30 6 8 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 10.69 8.70 66.212 75.794 2.49 5.509 1.758 1.601 8.508 1.520 13.55 2.120 1.920 1.450 3.108 53.482 1.623 9.74 52.76 108.00 224.00 235.000 349.00 1005 510 19.17 117.00 162.000 146.000 19.099 13.310 43.646 99.662 11.56 86.63 0.39 112.00 1595.00
31 6 14 67 older Male Low 3 No Yes BTT HVAD Alive s/p OHT alive 182319 40475 22.20 96.28 43.36 11.77 69.34 30.66 1.59 0.89 52.64 27.32 18.56 1.48 22.79 2.88 28.61 75.47 0.33 14.54 0.06 95.90 53.66 7.81 3.27 21.04 1.57 1.50 0.72 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
32 7 0 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 1073919 336538 31.34 100.00 63.29 14.80 93.05 6.95 0.73 8.05 75.49 4.55 19.16 0.80 7.25 8.78 28.04 88.72 2.51 5.18 1.02 90.49 68.18 2.32 4.09 1.69 3.48 1.27 5.78 103.00 34.40 73.796 17.487 330.00 74.260 18.401 16.683 51.466 82.515 12.80 150.000 15.178 15.208 9.970 26.235 21.107 8.44 133.00 39.26 415.00 111.000 298.00 443 296 138.00 125.00 10.503 37.240 39.468 13.629 111.000 409.000 77.43 78.89 10.95 301.00 5998.00
33 7 1 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 270301 97229 35.97 91.63 18.43 61.67 97.68 2.32 0.20 5.85 69.45 1.63 28.19 0.73 27.57 21.43 17.05 84.32 7.26 2.73 0.84 99.44 97.60 0.54 2.64 1.51 3.63 0.29 3.40 16.38 6.78 46.507 34.398 28.79 11.752 0.814 4.999 1481.000 8.712 8.92 17.769 2.325 4.279 2.360 63.109 16.386 5.90 34.66 152.00 295.00 55.059 278.00 1037 409 21.48 40.32 6.177 30.898 20.452 2.676 177.000 224.000 36.40 47.22 0.92 160.00 5604.00
34 7 3 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 499016 78019 15.63 89.41 25.83 51.38 92.98 7.02 0.32 4.66 51.34 6.31 41.59 0.76 36.49 5.96 22.81 73.48 6.73 7.93 0.33 98.78 96.52 1.31 2.06 5.19 2.68 0.59 2.45 16.38 6.52 31.962 18.537 34.49 16.410 2.113 4.287 78.861 9.272 9.04 17.769 2.902 2.861 3.595 11.983 22.669 9.71 14.43 38.26 183.00 56.470 219.00 307 219 29.76 47.34 5.053 3.891 7.570 2.878 33.464 191.000 16.92 81.03 0.92 99.54 4583.00
35 7 8 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 115825 32513 28.07 91.04 44.61 15.76 80.52 19.48 4.16 7.82 49.91 18.37 30.52 1.20 14.87 3.33 30.19 61.08 8.70 12.56 1.58 89.80 76.71 2.87 6.07 11.12 8.08 1.59 1.17 37.61 21.91 70.846 24.832 57.61 38.448 5.988 8.685 44.610 13.488 17.95 55.857 7.772 7.570 7.238 14.443 23.376 10.40 81.88 77.46 332.00 132.000 332.00 1453 632 38.47 104.00 12.646 40.206 31.261 7.509 79.659 354.000 26.71 180.00 0.92 274.00 7156.00
36 7 21 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 407080 97651 23.99 91.29 66.96 12.20 84.66 15.34 2.88 5.60 61.89 13.73 22.65 1.74 20.16 7.23 29.39 68.49 5.28 10.60 1.17 92.50 54.35 3.48 6.53 9.61 8.34 1.85 2.83 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
37 8 0 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 515760 266115 51.60 96.30 69.66 8.57 72.22 27.78 6.26 4.77 43.09 26.01 28.26 2.63 16.67 2.67 28.25 50.35 0.41 12.78 4.47 84.76 19.47 9.56 4.55 8.29 0.87 2.18 0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
38 8 8 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 539521 223872 41.49 96.72 60.02 12.63 80.13 19.87 1.66 2.48 43.56 19.52 35.24 1.68 20.46 3.67 20.54 55.76 0.18 10.80 2.43 87.82 13.25 6.55 5.96 4.65 0.95 2.51 0.65 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
39 8 21 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 564317 216312 38.33 97.40 55.80 4.83 74.19 25.81 5.35 5.77 37.90 25.72 34.76 1.62 17.99 2.18 31.75 61.61 0.77 13.44 6.97 76.55 6.97 10.34 11.14 4.46 0.94 5.14 2.43 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
40 9 0 49 younger Male Low 3 No No BTT HVAD Alive s/p OHT alive 89251 65181 73.03 76.76 84.53 1.15 74.65 25.35 1.91 1.39 55.03 22.40 19.62 2.95 15.65 6.80 25.85 70.49 0.87 23.26 0.23 83.16 25.00 8.85 5.03 11.63 1.56 2.95 3.08 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
41 9 3 49 younger Male Low 3 No No BTT HVAD Alive s/p OHT alive 375167 234817 62.59 98.46 60.29 10.18 87.72 12.28 0.46 0.48 68.54 9.90 19.03 2.54 30.58 19.69 15.54 72.77 0.13 20.35 0.03 92.56 36.28 3.75 2.70 9.17 1.19 1.27 0.63 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
42 9 5 49 younger Male Low 3 No No BTT HVAD Alive s/p OHT alive 256306 162176 63.27 93.82 68.43 8.45 85.45 14.55 0.70 0.53 71.99 11.98 13.37 2.66 32.41 17.96 12.46 69.74 0.41 24.63 0.02 95.72 44.12 4.47 2.23 12.99 1.30 1.20 0.77 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
43 9 8 49 younger Male Low 3 No No BTT HVAD Alive s/p OHT alive 463136 330714 71.41 98.79 70.51 7.30 87.51 12.49 1.78 0.58 69.60 10.61 17.82 1.97 29.83 14.55 12.59 75.03 0.15 21.61 0.07 93.59 30.94 4.05 3.07 10.00 1.08 1.35 0.62 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
44 10 0 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 92221 27988 30.35 85.45 55.78 1.51 85.56 14.44 0.28 0.28 55.28 4.72 39.72 0.28 0.00 1.89 1.89 89.44 0.00 2.78 0.00 15.00 0.00 3.33 0.83 0.28 0.28 3.33 0.00 7.64 8.05 12.160 18.210 2.65 2.810 2.640 2.960 12.830 1.500 2.82 2.930 1.340 0.600 2.050 88.780 4.370 4.15 28.83 39.26 873.00 84.790 386.00 413 361 15.64 102.00 3.080 25.630 12.440 3.660 40.120 112.000 84.03 48.41 2.75 67.22 13502.00
45 10 1 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 273746 46755 17.08 93.25 50.14 7.57 88.27 11.73 0.09 2.12 54.52 3.36 42.00 0.12 8.40 6.87 0.00 92.00 0.12 3.45 0.00 3.94 11.43 1.12 0.64 2.76 0.33 2.15 0.00 1.74 3.20 8.390 13.870 2.65 2.810 2.640 2.960 5.480 1.500 2.63 1.040 0.420 0.020 0.960 56.230 6.110 2.78 12.80 22.38 257.00 109.000 212.00 207 352 2.90 59.32 3.080 10.480 4.350 2.300 35.160 31.280 16.68 40.19 1.22 37.18 3646.00
46 10 3 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 156053 71752 45.98 78.38 35.43 27.12 78.57 21.43 0.24 0.60 58.56 5.80 18.33 17.31 12.67 72.96 0.53 94.06 0.49 5.11 0.28 22.79 27.47 0.81 5.53 0.01 0.29 0.19 0.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
47 10 5 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 28.26 29.24 25.930 19.130 12.01 9.860 4.910 2.960 12.270 2.300 8.31 8.480 4.420 6.460 5.600 31.690 12.300 6.41 65.76 22.02 605.00 111.000 650.00 279 320 27.98 245.00 8.330 34.600 26.450 10.410 112.000 184.000 48.97 197.00 24.56 128.00 8387.00
48 10 8 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 156053 71752 45.98 78.38 35.43 27.12 78.57 21.43 0.24 0.60 58.56 5.80 18.33 17.31 12.67 72.96 0.53 94.06 0.49 5.11 0.28 22.79 27.47 0.81 5.53 0.01 0.29 0.19 0.00 70.58 65.90 45.390 26.340 33.60 24.140 10.790 5.940 18.220 4.580 16.86 22.040 10.800 17.200 14.210 33.990 19.940 11.61 146.00 23.83 661.00 105.000 765.00 325 270 45.41 414.00 17.810 51.840 49.970 22.030 205.000 266.000 76.21 266.00 48.57 276.00 9997.00
49 11 3 60 younger Female High 2 Yes Yes BTT PVAD Died dead 116462 41076 35.27 95.44 33.79 25.24 84.51 15.49 2.92 0.99 45.34 13.19 39.12 2.36 17.56 8.75 19.73 79.35 5.17 10.80 0.71 90.04 29.59 10.16 6.81 8.21 3.37 4.86 4.47 16.71 5.07 550.000 96.245 2.72 2.500 6.486 1.361 62.319 3.478 19.00 32.659 1.350 3.356 1.533 254.000 17.441 6.13 35.07 87.82 55.52 228.000 423.00 7084 1125 10.10 84.06 30.293 26.765 20.856 5.302 78.434 153.000 3.21 132.00 1.92 103.00 710.00
50 11 5 60 younger Female High 2 Yes Yes BTT PVAD Died dead 222650 85649 38.47 98.40 32.09 19.82 77.54 22.46 5.02 1.10 43.96 19.20 33.50 3.34 17.47 9.77 15.63 71.89 6.68 14.08 0.70 86.49 27.87 13.04 9.84 12.30 4.35 7.98 6.39 9.78 8.45 727.000 112.000 2.72 2.500 7.387 1.361 70.374 2.483 35.08 18.389 1.350 3.356 1.378 367.000 16.270 7.93 41.30 108.00 183.00 241.000 526.00 5632 1295 12.58 77.86 46.047 25.264 23.381 4.874 96.343 142.000 4.47 197.00 1.92 79.15 769.00
51 11 8 60 younger Female High 2 Yes Yes BTT PVAD Died dead 102274 63057 61.65 97.73 27.89 20.66 73.75 26.25 7.33 3.45 35.79 21.92 37.73 4.56 9.97 8.20 16.96 63.96 2.40 11.16 3.07 73.97 5.92 16.69 17.77 11.74 4.90 12.19 6.09 8.08 5.07 592.000 95.220 2.81 2.500 9.032 1.270 88.002 2.970 30.12 13.849 1.350 2.217 1.612 342.000 16.660 5.71 31.97 90.34 159.00 194.000 472.00 4304 1610 4.14 95.92 35.248 54.219 22.115 4.660 88.759 110.000 2.67 143.00 1.92 115.00 702.00
52 11 14 60 younger Female High 2 Yes Yes BTT PVAD Died dead 133530 81665 61.16 96.46 22.03 16.15 35.30 64.70 13.74 2.33 8.48 52.28 26.37 12.87 5.29 5.79 19.15 47.48 5.65 17.83 5.43 39.88 6.08 52.08 62.11 37.45 21.95 50.15 3.84 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
53 12 0 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive 481928 162052 33.63 99.70 64.73 5.05 3.91 96.09 8.89 1.34 0.72 94.99 3.14 1.15 4.07 0.84 0.32 90.28 2.84 1.58 1.27 6.82 9.17 14.61 90.11 4.95 5.71 13.01 0.51 13.27 17.11 204.000 22.584 83.74 2.970 7.222 1.989 113.000 2.290 35.01 208.000 1.165 5.160 2.360 23.895 10.191 3.42 54.53 61.90 250.00 205.000 697.00 388 201 40.18 50.81 11.088 68.713 35.373 1.364 48.096 767.000 84.43 69.91 190.00 798.00 3051.00
54 12 1 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 9.96 64.103 28.423 5.76 2.970 0.420 1.989 477.000 2.290 13.19 3.190 0.940 0.850 2.360 85.133 16.242 2.59 30.46 156.00 455.00 67.295 507.00 225 483 19.92 62.77 6.118 35.321 8.503 1.901 237.000 258.000 31.01 36.77 18.08 131.00 4568.00
55 12 3 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 43.64 27.96 177.000 51.350 85.07 5.509 4.877 4.159 33.230 9.696 23.76 162.000 3.963 10.605 3.492 26.609 14.056 6.09 99.90 81.00 1152.00 204.000 307.00 854 1019 63.53 142.00 23.341 95.130 65.342 9.657 93.343 633.000 79.44 262.00 147.00 284.00 5911.00
56 12 5 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 7.16 6.78 8.658 17.038 2.80 2.970 1.553 1.231 28.927 3.330 3.48 4.260 1.165 2.060 2.360 30.095 2.770 5.31 49.41 39.91 376.00 64.661 398.00 334 344 20.45 40.32 3.050 12.574 11.877 1.901 18.625 158.000 40.18 183.00 0.92 74.91 10443.75
57 12 8 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive 38113 21486 56.37 97.86 58.44 7.88 20.47 79.53 8.09 1.69 1.21 75.00 18.96 4.83 10.07 2.59 0.53 82.19 2.29 9.00 3.59 15.16 0.00 23.07 63.95 4.77 6.52 16.91 0.40 7.16 9.15 39.545 15.389 5.76 2.970 1.399 1.814 11.147 2.723 8.55 7.477 1.430 0.850 2.360 15.487 3.399 2.59 37.42 29.78 548.00 107.000 502.00 225 445 25.83 57.69 3.617 19.015 22.505 1.717 40.247 340.000 56.01 128.00 18.84 96.76 10443.75
58 12 21 36 younger Male High 2 Yes NA BTT PVAD Alive s/p OHT alive 80991 19210 23.72 99.00 50.61 3.50 12.33 87.67 14.74 4.81 1.05 85.41 11.13 2.41 8.48 0.52 1.56 80.00 8.27 3.76 7.41 13.68 0.00 30.83 77.74 10.83 14.14 19.85 2.81 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
59 13 0 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 411616 119210 28.96 90.82 73.63 5.88 74.30 25.70 2.80 6.46 57.94 19.20 18.72 4.13 19.10 15.42 15.30 53.51 13.78 25.19 0.21 47.23 53.77 5.75 5.92 22.63 3.46 2.55 0.74 74.33 630.00 1198.000 29.033 285.00 6.710 41.890 282.000 58.746 108.000 242.00 1363.000 17.372 74.166 4.089 381.000 11.334 76.55 183.00 145.00 703.00 339.000 938.00 642 424 191.00 439.00 51.380 409.000 169.000 31.768 254.000 3217.000 299.00 1084.00 546.00 877.00 6887.00
60 13 1 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 430509 79492 18.46 89.31 52.10 20.45 87.13 12.87 1.23 4.76 58.11 9.15 30.55 2.20 29.86 18.05 10.37 63.63 10.23 17.43 0.06 74.05 72.50 2.36 2.98 10.68 1.72 1.03 2.89 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
61 13 3 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 395713 119670 30.24 97.90 23.79 6.27 82.17 17.83 0.79 1.99 49.95 13.01 33.91 3.13 28.36 16.83 5.00 56.33 7.13 26.73 0.02 79.50 82.88 1.96 5.55 16.30 3.61 1.88 0.42 4.28 159.00 409.000 41.124 12.27 2.320 4.307 20.356 15.581 16.793 121.00 61.854 2.899 4.794 1.980 49.157 20.020 28.46 32.56 102.00 524.00 415.000 305.00 282 465 41.67 203.00 12.431 121.000 35.967 2.120 73.622 945.000 39.60 451.00 55.87 159.00 676.00
62 13 8 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 329840 73468 22.27 98.63 70.61 3.73 51.50 48.50 8.32 3.77 33.46 41.29 20.92 4.33 25.19 5.89 24.07 46.40 3.73 34.64 0.07 64.14 28.43 10.35 9.61 38.78 4.62 5.55 2.91 100.00 430.00 1063.000 57.719 245.00 135.000 21.182 84.771 41.826 160.000 229.00 580.000 22.030 72.412 10.670 229.000 25.806 54.08 223.00 143.00 966.00 310.000 777.00 1415 796 217.00 325.00 45.047 411.000 137.000 44.518 266.000 2406.000 193.00 908.00 321.00 732.00 7464.00
63 13 14 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 350294 88823 25.36 97.63 74.37 4.37 73.35 26.65 4.91 4.83 56.54 21.32 19.84 2.30 34.68 11.25 18.37 61.98 5.78 20.55 0.04 63.91 47.54 6.81 6.17 21.77 2.90 2.74 3.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
64 14 0 38 younger Male High 2 Yes Yes BTT TAH Died s/p OHT dead 128451 60927 47.43 96.82 60.77 4.70 77.26 22.74 2.17 1.95 55.58 18.40 21.54 4.47 27.86 18.89 15.33 69.90 0.94 24.40 0.14 70.73 1.85 8.01 8.66 20.21 5.45 3.32 0.39 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
65 14 1 38 younger Male High 2 Yes Yes BTT TAH Died s/p OHT dead 142796 22573 15.81 80.67 47.31 2.28 63.37 36.63 9.16 6.51 26.02 33.49 37.11 3.37 38.13 7.50 13.13 53.25 5.78 26.27 1.57 40.24 0.00 27.71 22.17 29.16 18.80 15.66 8.11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
66 14 3 38 younger Male High 2 Yes Yes BTT TAH Died s/p OHT dead 157167 38008 24.18 93.47 34.86 3.95 69.71 30.29 3.92 3.92 33.64 26.30 35.78 4.28 39.54 11.49 14.94 66.86 2.85 24.31 1.04 64.50 7.27 21.60 20.53 27.66 14.54 14.18 2.12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
67 14 5 38 younger Male High 2 Yes Yes BTT TAH Died s/p OHT dead 121823 23097 18.96 93.48 2.51 2.87 99.52 0.48 0.00 0.00 0.97 0.48 98.55 0.00 50.00 25.00 25.00 94.35 0.00 2.58 0.00 0.16 0.00 17.45 87.40 0.00 0.16 18.26 0.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
68 14 8 38 younger Male High 2 Yes Yes BTT TAH Died s/p OHT dead 196515 88579 45.07 85.54 17.17 3.28 59.38 40.62 3.21 1.41 35.44 35.28 23.87 5.42 62.85 12.32 3.71 66.05 4.38 23.22 0.27 74.53 31.43 25.51 28.12 39.78 25.79 21.57 0.79 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
69 15 0 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.18 33.63 188.000 37.494 2.31 5.509 0.910 1.590 29.263 1.605 50.08 10.790 1.920 1.782 1.980 112.000 13.845 6.25 8.93 62.53 69.30 213.000 403.00 1373 458 5.90 36.01 7.766 139.000 6.036 0.944 37.403 629.000 9.39 257.00 0.39 40.05 19.79
70 15 1 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive 87827 15751 17.93 76.38 31.60 12.17 77.46 22.54 6.35 2.53 50.34 20.22 27.05 2.39 19.68 7.62 19.05 75.55 7.38 9.49 0.71 94.26 89.19 11.68 17.96 18.78 6.97 10.86 12.46 10.69 5.04 118.000 49.561 3.51 52.670 1.125 2.413 349.000 3.799 22.94 23.573 1.950 1.782 2.042 443.000 22.377 3.62 41.79 165.00 206.00 137.000 323.00 1573 1461 10.05 112.00 12.371 72.373 20.483 3.987 364.000 322.000 10.50 82.02 0.39 77.29 380.00
71 15 3 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive 55539 13608 24.50 79.00 28.02 15.78 81.90 18.10 6.13 2.42 66.92 14.33 14.98 3.77 20.81 17.45 18.12 78.01 7.08 8.61 0.72 94.04 70.73 11.32 16.69 15.21 5.78 10.50 16.39 6.39 3.54 119.000 48.334 2.31 220.000 1.369 1.590 57.616 1.520 28.03 7.706 1.920 1.450 1.980 158.000 19.806 5.94 27.01 132.00 296.00 172.000 322.00 1138 1362 3.98 63.62 6.549 69.626 14.327 3.358 80.780 351.000 5.73 82.02 0.39 40.05 86.64
72 15 5 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 5.33 34.44 200.000 52.572 3.51 474.000 2.603 1.773 79.861 1.605 55.35 7.706 1.920 1.494 2.382 177.000 14.690 7.33 45.46 166.00 572.00 205.000 488.00 1047 1280 10.05 102.00 4.485 119.000 19.099 7.153 161.000 644.000 9.39 221.00 0.39 83.29 162.00
73 15 8 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive 295005 36785 12.47 94.58 36.55 9.14 86.32 13.68 2.45 1.82 37.01 12.52 49.18 1.29 16.24 4.71 12.47 8.36 1.73 6.64 0.81 84.78 46.55 12.52 9.28 5.94 1.73 4.47 2.49 6.39 108.00 240.000 53.791 2.31 408.000 2.900 1.686 53.531 2.648 69.91 77.150 3.312 4.794 1.980 165.000 11.334 12.51 30.71 150.00 348.00 270.000 546.00 1549 1547 23.97 71.06 2.980 140.000 20.483 4.619 182.000 735.000 18.04 283.00 0.39 83.29 86.64
74 15 14 61 older Male High 1 Yes No BTT TAH Alive s/p OHT alive 60695 20471 33.73 94.91 6.93 7.42 83.97 16.03 3.40 0.69 24.36 14.71 59.33 1.60 18.43 5.07 11.52 54.82 2.91 16.10 0.50 82.44 30.00 51.08 13.25 10.41 3.68 11.31 6.06 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
75 16 0 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 52016 30920 59.44 95.77 55.57 24.62 58.73 41.27 2.14 0.97 53.01 5.05 5.45 36.50 9.39 81.29 2.88 81.48 5.65 9.02 0.48 97.75 71.83 0.67 22.89 0.34 1.78 0.49 NA 11.66 5.99 5.180 18.210 2.65 2.810 2.640 2.960 2.600 1.500 3.19 3.790 0.750 1.140 1.270 12.840 1.920 2.78 22.29 13.36 205.00 120.000 433.00 852 235 13.38 108.00 17.980 22.860 7.890 3.660 26.920 114.000 27.36 63.49 6.10 47.28 2960.00
76 16 1 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 75516 48801 64.62 92.13 52.90 18.64 70.39 29.61 2.92 2.21 62.18 5.13 7.98 24.70 13.79 69.54 3.19 84.14 3.64 6.36 0.41 94.93 43.78 0.57 15.61 0.23 0.91 0.36 NA 6.69 4.54 3.850 13.310 2.65 2.810 2.640 2.960 15.360 1.500 2.89 2.390 0.640 1.550 1.060 51.270 3.890 2.78 23.91 29.12 139.00 53.210 184.00 503 192 13.38 162.00 30.710 25.630 7.270 3.380 58.260 135.000 10.50 52.34 4.60 111.00 1306.00
77 16 3 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 250612 130454 52.05 95.75 60.05 3.29 46.04 53.96 1.34 3.89 33.08 32.47 8.29 26.15 3.56 34.61 0.09 76.71 0.39 13.20 0.28 39.79 14.38 2.26 1.56 10.45 2.89 3.04 0.08 22.58 5.49 6.580 15.690 2.77 2.810 2.640 2.960 19.820 1.500 3.96 4.710 1.750 5.060 1.270 242.000 7.410 6.23 43.70 45.47 205.00 139.000 201.00 614 498 27.61 259.00 37.910 41.540 13.790 4.520 90.330 187.000 20.67 91.18 20.95 94.90 1637.00
78 16 5 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 36.87 8.59 9.500 14.150 5.49 4.340 2.640 2.960 3.860 1.500 4.92 8.120 2.050 5.620 1.480 49.360 5.860 3.77 51.10 19.90 214.00 142.000 261.00 1163 556 27.24 145.00 37.770 41.540 20.750 5.700 54.980 210.000 28.80 99.93 38.44 100.00 3060.00
79 16 8 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 212478 120477 56.70 85.12 48.36 12.51 79.57 20.43 1.35 2.14 58.73 12.87 18.44 9.96 5.63 34.32 0.11 86.74 0.41 6.88 0.15 35.30 53.09 1.23 0.75 2.36 1.04 1.47 NA 39.75 7.52 10.260 14.990 6.70 4.900 2.640 3.060 5.960 1.500 5.57 11.870 2.670 6.610 1.480 49.000 5.860 3.86 55.20 17.87 278.00 105.000 428.00 1180 309 30.12 162.00 41.370 49.350 22.170 5.990 67.210 220.000 34.39 108.00 31.81 119.00 3873.00
80 17 0 70 older Female High 2 No NA DT HMII Alive alive 86135 70306 81.62 93.71 74.50 1.59 61.74 38.26 1.91 2.67 33.97 14.50 26.81 24.71 20.67 62.47 9.03 73.00 2.00 24.71 1.27 20.23 71.43 0.48 12.79 0.38 3.24 0.48 4.60 2.81 7.26 7.290 173.000 2.65 2.810 2.640 2.960 12.550 1.500 4.28 8.850 0.640 0.770 1.160 2.930 3.890 3.28 27.19 22.83 145.00 70.170 765.00 1478 482 9.57 94.83 94.860 103.000 8.520 3.660 18.060 120.000 19.71 40.19 28.19 51.59 1276.00
81 17 1 70 older Female High 2 No NA DT HMII Alive alive 81004 41334 51.03 94.06 53.64 2.99 61.70 38.30 3.10 2.75 39.07 13.34 22.12 25.47 12.58 63.11 15.99 78.92 2.93 17.38 3.35 23.24 71.88 0.52 11.70 0.09 1.29 0.52 9.83 7.64 4.08 4.170 41.980 2.65 2.810 2.640 2.960 133.000 1.500 2.63 7.760 0.420 1.770 0.910 98.210 8.740 4.54 15.89 77.14 211.00 64.900 374.00 1714 959 5.00 64.16 21.350 54.290 8.520 2.560 284.000 52.100 11.10 21.92 7.15 51.59 1301.00
82 17 3 70 older Female High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.22 3.20 3.220 21.580 2.65 2.810 2.640 2.960 15.210 1.500 2.63 2.520 0.420 0.020 0.680 58.680 7.800 3.77 8.37 31.94 83.16 74.380 388.00 644 432 2.90 49.55 8.660 30.220 4.630 1.790 14.940 45.600 11.69 14.26 1.53 29.96 690.00
83 17 5 70 older Female High 2 No NA DT HMII Alive alive 90024 39434 43.80 95.32 68.83 5.62 71.59 28.41 0.85 1.66 35.23 13.02 35.80 15.96 22.94 52.37 5.85 76.42 1.14 21.83 0.62 14.54 48.57 0.38 4.69 0.00 0.80 0.38 0.62 0.58 3.63 3.220 25.010 2.65 2.810 2.640 2.960 23.480 1.500 2.63 2.390 0.420 0.100 0.860 31.820 7.670 11.61 15.89 41.91 454.00 91.330 506.00 670 421 3.21 54.44 14.370 37.430 6.660 2.300 33.500 58.260 15.08 19.42 6.10 41.52 1837.00
84 17 8 70 older Female High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 37.61 21.91 44.071 23.633 39.20 9.532 6.428 5.102 32.860 7.062 12.93 20.414 2.167 3.138 5.978 41.851 8.190 4.95 63.21 39.70 328.00 175.000 323.00 808 540 34.88 91.67 13.604 48.125 30.231 5.864 59.949 466.000 81.74 150.00 4.51 195.00 9741.00
85 17 14 70 older Female High 2 No NA DT HMII Alive alive 106791 80532 75.41 88.82 79.92 1.57 62.32 37.68 1.70 4.02 28.04 22.86 34.11 15.00 23.92 39.41 15.03 61.61 3.66 34.55 1.97 20.36 73.33 0.80 8.57 0.27 1.79 0.98 0.71 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
86 18 0 63 older Male High 1 No NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 60.38 32.81 186.000 38.833 32.98 21.709 3.051 5.621 37.885 11.091 29.33 189.000 6.338 16.504 4.089 278.000 17.668 7.17 59.99 54.99 695.00 232.000 211.00 828 1145 50.86 107.00 10.251 111.000 31.722 7.153 120.000 533.000 28.75 322.00 2.34 224.00 3524.00
87 18 1 63 older Male High 1 No NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 63.95 40.14 249.000 28.453 32.98 21.709 3.357 4.711 28.562 11.091 38.39 220.000 6.089 13.032 4.089 124.000 15.114 5.01 59.99 41.74 638.00 289.000 303.00 509 713 59.63 121.00 9.867 123.000 56.352 7.153 82.196 685.000 46.72 384.00 14.39 229.00 5489.00
88 18 3 63 older Male High 1 No NA BTT HMII Died dead 366713 77282 21.07 98.61 66.55 12.44 93.65 6.35 0.53 0.60 62.22 4.68 32.38 0.72 19.94 9.89 28.23 83.18 0.51 14.38 0.30 94.72 54.39 2.07 11.33 2.86 3.40 1.48 3.58 65.71 24.75 198.000 26.329 40.68 18.837 4.631 4.711 20.178 9.696 29.66 185.000 5.841 11.411 4.293 71.642 12.166 3.92 74.20 35.60 1110.00 222.000 300.00 489 627 50.86 126.00 9.479 104.000 51.467 10.275 77.933 727.000 62.42 422.00 11.03 219.00 9557.42
89 18 5 63 older Male High 1 No NA BTT HMII Died dead 262251 55344 21.10 99.55 62.37 14.39 93.00 7.00 1.03 0.69 65.71 4.74 28.37 1.17 25.93 17.28 25.93 79.87 1.20 17.05 0.48 97.76 58.18 2.27 11.13 4.12 4.91 1.87 6.10 89.92 70.33 309.000 36.344 103.00 29.010 8.732 7.037 28.329 24.712 57.19 384.000 8.403 25.681 5.347 93.592 18.950 6.40 67.14 51.06 434.00 238.000 328.00 896 536 91.53 134.00 14.161 155.000 92.272 10.890 115.000 873.000 64.87 398.00 65.30 368.00 2247.00
90 18 8 63 older Male High 1 No NA BTT HMII Died dead 309193 61091 19.76 98.15 56.62 7.24 83.61 16.39 3.27 1.52 58.03 11.90 28.13 1.93 24.65 11.36 1.52 79.53 1.45 13.33 0.97 92.63 25.76 3.50 9.23 7.57 3.73 3.48 0.00 60.38 17.64 183.000 34.233 42.23 20.268 6.645 4.488 29.029 11.795 27.05 177.000 6.089 10.605 3.887 154.000 12.585 3.77 56.39 56.34 1534.00 165.000 446.00 487 478 53.19 121.00 10.759 85.242 37.382 13.310 79.359 700.000 104.00 490.00 12.72 151.00 9557.42
91 19 0 68 older Male Low 3 No NA DT HMII Died dead 147997 54649 36.93 91.75 79.63 7.62 74.86 25.14 0.60 3.45 62.45 22.87 12.30 2.38 45.98 9.28 13.61 74.33 2.67 21.19 0.84 91.31 90.15 3.58 2.25 21.79 1.36 0.58 0.34 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
92 19 1 68 older Male Low 3 No NA DT HMII Died dead 177687 56400 31.74 97.13 69.76 12.60 81.83 18.17 0.65 1.61 55.77 16.13 26.02 2.09 53.19 10.70 11.57 82.92 1.38 13.98 0.28 89.83 85.59 3.78 1.85 14.65 1.30 0.68 0.18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
93 19 3 68 older Male Low 3 No NA DT HMII Died dead 233155 67140 28.80 98.44 74.89 5.32 81.96 18.04 1.00 1.88 49.03 16.59 32.90 1.48 49.92 7.20 12.83 83.44 1.39 12.98 0.14 89.70 69.70 4.58 1.62 13.89 1.08 1.08 0.17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
94 19 5 68 older Male Low 3 No NA DT HMII Died dead 99257 29455 29.68 97.33 62.48 4.73 80.60 19.40 1.84 1.25 45.06 18.36 35.25 1.33 44.61 6.32 12.27 82.89 0.88 13.20 0.09 83.55 35.29 7.23 3.54 14.75 2.29 3.02 1.20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
95 19 8 68 older Male Low 3 No NA DT HMII Died dead 76358 18702 24.49 97.32 29.48 11.10 86.29 13.71 2.43 0.50 37.92 12.13 48.32 1.63 46.45 11.70 11.35 83.96 0.20 10.50 0.06 57.82 50.00 9.95 29.60 10.20 0.64 9.36 0.80 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
96 20 0 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead 56374 12141 21.54 67.38 27.12 3.32 51.84 48.16 1.84 1.84 2.94 45.59 47.79 3.68 0.00 0.00 2.29 92.65 0.37 5.51 0.72 30.15 60.00 6.99 9.56 0.37 0.00 3.68 0.00 2.22 9.41 292.000 21.883 2.31 2.320 1.245 1.590 7.255 1.520 70.36 2.120 1.920 1.450 1.980 90.424 1.623 3.92 8.93 91.17 78.30 141.000 579.00 666 562 10.05 55.48 11.010 72.373 9.732 2.120 40.533 733.000 2.89 164.00 0.39 33.64 518.00
97 20 1 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 7.16 11.35 124.000 35.144 2.80 2.970 0.552 1.231 25.459 2.290 28.30 3.190 0.940 0.850 2.360 219.000 2.770 10.35 40.14 133.00 229.00 86.923 599.00 801 488 22.49 26.21 38.398 66.074 17.389 2.089 52.042 488.000 5.07 124.00 0.92 59.20 1144.00
98 20 3 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead 7018 3464 49.36 89.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.66 13.04 89.555 29.993 4.95 2.970 1.247 1.474 18.328 2.432 24.88 3.190 2.486 0.850 2.593 45.524 4.181 8.44 60.77 134.00 181.00 94.833 598.00 549 407 18.84 57.69 110.000 103.000 18.917 3.601 45.144 420.000 10.11 113.00 0.92 54.13 1900.00
99 20 5 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 10.69 16.87 180.000 45.496 4.61 2.320 1.825 1.686 21.569 2.111 48.20 2.120 1.920 1.450 2.382 148.000 2.224 14.68 41.79 117.00 278.00 137.000 520.00 821 424 13.47 107.00 34.287 84.751 13.657 6.521 58.902 618.000 8.24 146.00 0.39 65.11 1301.00
100 20 8 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead 80210 21828 27.21 85.74 20.38 3.50 61.98 38.02 8.55 2.90 5.50 34.96 56.18 3.36 0.00 0.00 3.21 89.31 0.61 2.60 1.49 23.66 0.00 21.37 9.16 1.07 0.15 5.34 1.31 132.00 18.42 230.000 38.833 20.88 7.954 3.203 8.499 16.494 4.406 56.12 2.120 26.710 15.890 5.564 79.472 18.095 17.57 118.00 101.00 250.00 163.000 563.00 804 352 72.23 160.00 30.736 184.000 43.034 13.310 76.501 902.000 49.14 176.00 58.43 277.00 6159.00
101 20 14 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead 55068 19145 34.77 75.16 16.90 3.30 64.00 36.00 4.21 2.11 10.53 34.11 52.42 2.95 0.00 0.00 2.94 92.42 0.00 3.37 1.00 22.74 0.00 12.42 11.16 0.63 0.00 4.63 0.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
102 20 21 37 younger Female High 2 Yes NA BTT PVAD Died s/p OHT dead 76673 23615 30.80 83.39 26.27 2.06 63.55 36.45 5.42 5.42 5.17 34.48 58.13 2.22 0.00 0.00 2.72 89.41 0.00 3.45 1.95 18.23 4.55 18.23 8.87 1.23 0.25 4.68 0.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
103 21 0 46 younger Male High 2 No NA DT HMII Alive alive 226762 25868 11.41 89.41 25.31 12.05 55.06 44.94 11.31 2.69 14.79 42.57 39.95 2.69 19.87 3.88 32.88 68.95 10.48 13.57 0.59 80.62 22.67 31.91 19.38 15.58 10.37 16.55 8.18 2.18 2.45 60.794 34.617 2.31 2.320 1.245 1.590 68.832 1.520 7.45 2.120 1.920 1.450 1.980 130.000 3.544 14.61 5.57 47.27 442.00 52.814 249.00 328 245 3.98 55.48 2.230 19.937 2.737 3.987 21.566 118.000 2.89 23.91 0.39 52.69 1293.00
104 21 3 46 younger Male High 2 No NA DT HMII Alive alive 244049 39386 16.14 94.94 40.46 13.23 40.93 59.07 13.36 3.17 8.33 54.90 32.28 4.49 18.52 4.21 23.83 55.18 13.87 19.39 1.44 70.53 35.67 33.62 33.47 22.86 15.26 25.81 5.11 2.18 2.71 17.955 26.715 9.57 2.320 1.893 1.590 3.759 1.520 3.89 2.120 1.920 1.450 1.980 59.228 1.623 7.17 8.93 60.31 820.00 73.754 165.00 347 262 3.98 63.62 2.230 18.052 2.737 3.358 15.234 90.169 11.56 77.19 0.39 33.64 2726.00
105 21 5 46 younger Male High 2 No NA DT HMII Alive alive 344736 250121 72.55 89.83 7.54 3.81 45.84 54.16 3.96 1.53 13.39 52.70 32.18 1.72 67.14 2.92 2.44 74.68 6.81 13.91 0.21 63.33 58.02 33.05 30.94 19.83 13.96 26.74 0.22 2.18 4.12 15.278 24.202 12.27 2.320 2.457 1.590 4.492 1.520 3.07 2.120 1.920 1.450 2.558 43.554 1.623 2.86 23.32 48.13 1082.00 71.954 125.00 260 251 5.90 77.95 2.230 18.052 7.234 7.153 27.925 153.000 29.97 118.00 0.39 52.69 4714.00
106 21 8 46 younger Male High 2 No NA DT HMII Alive alive 216934 140093 64.58 96.37 4.78 7.22 22.49 77.51 13.49 0.64 7.22 67.61 15.17 10.00 35.88 7.82 17.22 51.28 39.40 6.56 1.33 62.59 58.06 66.67 64.59 53.00 50.26 63.78 4.97 2.18 2.45 24.797 23.042 8.26 2.320 1.625 1.590 3.060 1.520 4.03 2.120 1.920 1.450 1.980 44.329 0.827 2.13 5.57 54.35 370.00 84.966 89.52 348 255 3.98 46.41 2.230 9.990 2.520 2.120 9.035 55.097 2.89 23.91 0.39 27.20 1113.00
107 21 14 46 younger Male High 2 No NA DT HMII Alive alive 215820 66785 30.94 92.54 17.20 6.01 25.88 74.12 9.15 1.45 3.09 71.72 22.44 2.74 65.43 3.42 0.04 69.38 15.93 8.85 0.74 50.58 24.07 52.78 48.88 32.28 28.19 44.04 0.04 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
108 21 21 46 younger Male High 2 No NA DT HMII Alive alive 670692 523014 77.98 96.05 5.07 2.64 23.61 76.39 4.11 0.49 4.79 73.53 18.68 3.00 42.40 2.72 4.49 71.95 20.81 5.51 0.45 48.22 44.62 60.13 60.26 39.07 36.82 56.38 1.49 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
109 22 0 75 older Male High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 59.97 33.76 44.886 35.293 48.50 30.282 52.148 11.702 46.900 18.224 10.78 72.616 5.569 5.458 11.016 23.895 17.965 7.15 143.00 43.59 989.00 135.000 584.00 662 925 40.52 163.00 13.354 43.750 59.591 13.629 94.278 449.000 104.00 196.00 0.92 248.00 10443.75
110 22 1 75 older Male High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 6.42 13.61 24.600 30.292 4.17 3.956 10.044 3.491 27.540 3.964 5.69 3.190 0.940 1.805 2.360 69.905 14.225 3.22 64.43 56.18 263.00 47.791 495.00 624 1321 16.57 80.75 6.352 20.019 15.869 3.706 57.971 119.000 6.20 184.00 0.92 49.17 2297.00
111 22 3 75 older Male High 2 No NA DT HMII Alive alive 121794 28129 23.10 97.05 52.79 16.42 70.08 29.92 0.29 0.54 50.71 21.46 22.96 4.86 34.18 12.51 23.49 39.78 0.31 55.18 0.35 96.01 16.67 1.74 5.27 23.38 2.95 1.00 0.10 2.77 13.90 16.630 13.668 9.16 2.970 3.372 2.078 10.277 6.347 2.66 4.260 1.165 1.090 2.360 36.910 7.340 2.59 60.77 27.10 698.00 100.000 514.00 323 606 25.83 54.27 4.276 23.867 33.318 4.024 31.548 264.000 61.66 124.00 0.92 69.60 10443.75
112 22 5 75 older Male High 2 No NA DT HMII Alive alive 82157 17295 21.05 99.20 72.54 7.41 68.21 31.79 1.10 0.55 48.47 22.90 23.60 5.04 31.90 13.57 18.10 37.84 0.47 56.73 0.35 92.37 14.29 2.05 7.79 26.20 4.72 1.42 1.02 17.15 11.58 21.356 25.652 9.57 5.509 5.963 3.002 17.410 6.315 3.89 23.573 2.504 1.494 5.132 87.134 12.166 2.86 56.39 33.82 845.00 150.000 484.00 556 814 19.17 130.00 6.685 27.906 20.483 10.890 40.533 185.000 70.35 99.39 2.34 118.00 7899.00
113 22 8 75 older Male High 2 No NA DT HMII Alive alive 154810 27067 17.48 98.04 72.18 5.03 74.91 25.09 0.90 1.65 58.05 14.76 20.82 6.37 23.43 25.71 22.57 44.94 0.60 49.36 0.71 86.59 22.73 3.30 7.64 20.15 4.87 1.72 0.00 10.19 18.30 30.677 25.655 16.47 9.100 6.693 4.896 29.159 3.022 8.07 3.688 1.295 1.557 8.079 68.056 13.360 5.90 58.29 31.79 671.00 47.791 696.00 506 704 18.84 102.00 12.748 17.993 11.877 5.864 44.162 198.000 49.71 115.00 1.39 114.00 10443.75
114 22 21 75 older Male High 2 No NA DT HMII Alive alive 197936 15446 7.80 95.86 50.35 5.44 79.13 20.87 3.98 3.11 52.30 15.90 29.81 1.99 20.23 5.78 24.86 40.62 1.12 50.06 1.89 62.36 8.00 7.20 9.94 13.17 3.60 5.09 1.55 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
115 23 0 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 114586 35996 31.41 100.00 59.03 11.31 75.87 24.13 1.52 7.69 69.83 11.67 10.20 8.30 27.80 29.94 2.04 84.64 0.64 10.54 0.61 91.57 61.34 4.99 7.15 11.57 5.92 2.53 1.92 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
116 23 1 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 13.27 4.09 22.842 12.996 2.80 2.970 1.098 0.346 6.097 2.290 3.48 3.190 0.940 0.850 2.360 103.000 4.849 4.64 2.91 28.25 276.00 25.145 449.00 274 442 7.15 31.49 3.050 10.241 2.290 1.036 12.701 13.318 2.92 110.00 0.92 39.59 1621.00
117 23 3 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 349054 129419 37.08 95.25 62.35 8.81 79.64 20.36 2.72 4.45 57.70 13.93 25.89 2.48 57.14 13.49 8.69 74.29 3.76 12.35 1.41 85.87 62.53 3.41 5.43 7.67 1.40 1.92 1.06 72.99 13.61 25.476 15.689 9.16 4.312 3.544 2.168 12.912 2.432 6.98 3.190 1.430 0.850 5.769 30.560 4.849 4.20 59.53 23.70 268.00 56.470 507.00 350 496 15.38 108.00 3.050 23.400 13.860 7.400 61.926 158.000 3.85 123.00 0.92 108.00 2374.00
118 23 5 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 447309 176789 39.52 93.00 53.57 9.32 80.92 19.08 2.16 5.73 56.72 12.51 28.43 2.34 47.68 12.19 11.00 73.80 4.44 12.53 2.57 83.88 49.09 5.24 6.82 8.32 2.18 2.28 1.72 75.52 39.53 49.720 16.363 80.35 13.585 14.246 8.044 28.002 22.225 15.11 23.132 3.869 4.279 13.928 30.947 13.071 8.12 149.00 16.40 384.00 120.000 681.00 419 400 41.84 179.00 5.172 46.471 46.582 14.665 126.000 364.000 36.57 89.46 22.99 224.00 4716.00
119 23 8 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 558290 301468 54.00 95.62 47.73 7.04 79.13 20.87 3.43 7.49 53.19 13.48 30.32 3.01 36.36 14.54 12.10 70.55 5.15 11.89 3.51 79.79 52.89 6.98 8.50 7.82 1.92 3.09 1.96 55.93 9.15 32.389 17.862 2.80 2.970 3.716 1.989 10.711 2.290 7.10 NA NA 12.466 2.360 17.125 3.019 4.20 57.05 18.04 905.00 265.000 855.00 393 618 22.99 71.07 3.050 22.929 32.290 4.131 50.068 170.000 NA 97.14 17.70 80.29 10443.75
120 23 14 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 479402 362845 75.69 97.95 44.30 6.30 76.53 23.47 3.90 11.10 34.99 12.04 50.31 2.66 21.93 14.88 10.05 70.21 4.66 7.31 7.73 49.96 29.38 13.44 17.37 5.64 3.01 6.95 2.28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
121 23 21 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 334942 191418 57.15 93.28 70.35 4.04 70.89 29.11 6.20 17.70 56.45 19.57 20.44 3.53 38.91 14.64 10.35 55.11 6.61 14.34 8.70 80.96 58.92 7.58 7.96 10.11 2.08 2.08 2.48 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
122 24 0 67 older Male High 2 Yes NA BTT HMII Died dead 129980 41600 32.00 91.40 39.03 2.85 49.63 50.37 7.02 11.55 28.37 29.11 20.79 21.72 19.43 28.34 9.80 69.59 4.90 20.33 6.13 31.15 33.60 20.15 22.27 0.28 4.25 16.54 2.09 33.99 28.59 16.040 25.430 12.01 7.920 6.660 3.530 19.960 3.020 7.39 10.910 3.870 5.620 7.350 29.050 11.190 6.77 80.92 26.34 485.00 156.000 684.00 574 531 28.71 233.00 9.210 37.430 28.250 15.220 112.000 158.000 41.01 59.87 50.92 139.00 4188.00
123 24 1 67 older Male High 2 Yes NA BTT HMII Died dead 59478 20182 33.93 89.07 44.50 10.08 60.93 39.07 2.10 2.70 42.88 13.69 17.66 25.77 13.06 46.12 3.13 81.35 1.16 16.11 0.83 37.58 6.12 5.52 11.64 0.06 1.49 2.76 4.07 4.02 3.20 2.320 19.970 2.65 2.810 2.640 2.960 405.000 1.500 2.63 2.130 0.460 0.450 0.860 99.660 13.000 2.78 12.80 206.00 266.00 84.610 329.00 197 841 3.21 39.72 8.990 27.180 6.360 2.830 565.000 23.210 11.10 10.23 1.22 38.63 1588.00
124 24 3 67 older Male High 2 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 6.69 9.68 5.520 72.480 2.65 2.810 2.640 2.960 12.830 1.500 3.81 3.490 3.700 1.140 1.870 30.080 8.600 3.57 26.36 120.00 339.00 166.000 633.00 484 808 12.17 90.21 79.330 79.230 12.440 4.520 64.780 75.100 48.83 38.04 18.56 55.88 3892.00
125 24 5 67 older Male High 2 Yes NA BTT HMII Died dead 26646 9754 36.61 98.69 58.32 7.57 64.88 35.12 2.06 0.55 46.50 12.62 17.70 23.18 15.07 53.31 12.50 83.40 1.10 15.64 1.76 28.12 0.00 7.54 16.46 0.55 1.92 6.04 13.00 0.58 3.20 3.220 34.140 2.65 2.810 2.640 2.960 39.370 1.500 2.63 2.930 0.420 0.020 0.540 66.160 8.470 3.38 5.65 51.46 66.05 106.000 497.00 1069 1138 2.90 29.91 6.510 29.470 5.480 1.550 51.690 2.870 3.21 8.86 43.82 41.52 535.00
126 24 8 67 older Male High 2 Yes NA BTT HMII Died dead 73641 21944 29.80 96.79 48.91 4.47 62.74 37.26 2.84 3.16 37.47 17.79 24.95 19.79 19.46 38.11 9.46 79.79 1.89 13.58 1.24 27.05 20.00 14.11 13.37 0.21 2.74 10.53 3.31 2.44 4.08 4.340 49.370 2.65 2.810 2.640 2.960 25.260 1.500 2.63 6.360 0.420 0.100 0.770 74.530 10.920 5.20 8.37 91.97 73.49 135.000 493.00 1207 1273 2.90 49.55 17.980 28.710 7.270 2.560 35.160 14.190 3.93 8.86 52.09 35.74 551.00
127 25 0 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 44004 20612 46.84 43.65 15.61 21.54 49.64 50.36 10.37 0.57 15.94 45.15 33.18 5.73 24.69 5.68 20.25 44.58 1.55 36.17 0.88 67.23 27.27 32.35 15.48 20.02 4.49 10.94 2.76 71.72 47.35 26.349 37.381 158.00 33.334 10.660 8.044 35.402 46.417 6.87 NA 10.690 8.792 2.398 99.342 9.045 6.48 97.08 46.45 297.00 181.000 191.00 567 222 49.74 80.75 6.059 37.993 25.593 20.817 174.000 399.000 44.78 92.77 41.52 NA 4980.00
128 25 1 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 99731 23424 23.49 91.81 41.33 10.89 35.24 64.76 11.83 1.62 18.71 59.21 16.19 5.89 28.16 5.89 18.53 39.98 4.66 42.16 2.33 84.07 2.63 22.55 15.21 40.28 6.07 9.70 3.96 29.43 17.11 20.632 25.281 48.50 22.252 5.900 4.692 222.000 11.168 3.91 72.616 4.422 2.060 2.360 191.000 11.918 7.37 68.02 138.00 324.00 136.000 149.00 347 223 30.17 80.75 4.036 30.061 12.370 7.619 280.000 331.000 21.43 70.73 2.56 274.00 3581.00
129 25 3 68 older Male High 2 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 74.26 46.69 17.521 32.607 125.00 34.355 9.340 7.512 18.328 13.879 4.56 190.000 13.031 14.904 2.360 35.125 11.629 8.85 37.42 35.14 218.00 222.000 173.00 1649 290 71.92 85.48 3.557 52.272 44.557 15.485 186.000 514.000 30.19 168.00 48.69 743.00 1283.00
130 25 5 68 older Male High 2 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 63.06 27.96 35.932 31.732 44.55 16.008 3.051 5.621 18.791 10.391 5.03 152.000 11.673 14.663 1.980 35.584 10.919 6.56 45.46 42.92 420.00 180.000 216.00 2058 291 95.02 121.00 5.314 63.342 43.034 23.444 164.000 411.000 28.75 257.00 28.67 330.00 1677.00
131 25 8 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 129637 50001 38.57 90.65 44.41 6.05 30.66 69.34 17.12 4.05 10.62 66.50 19.60 3.28 26.41 3.46 13.37 39.74 7.15 39.49 11.91 71.97 2.70 32.48 33.14 43.91 7.81 22.70 3.82 40.52 23.75 22.842 21.835 74.36 13.123 5.021 5.931 27.077 9.272 4.67 134.000 12.603 9.710 2.360 53.765 8.759 6.48 60.77 35.38 426.00 223.000 224.00 1195 211 57.75 93.20 3.857 32.945 29.201 15.485 185.000 523.000 35.17 121.00 27.51 602.00 4088.00
132 25 14 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 134368 46363 34.50 88.32 34.25 4.63 32.47 67.53 12.86 8.38 8.91 64.89 22.88 3.32 24.78 3.89 13.58 36.16 6.01 40.33 13.77 72.11 1.89 29.89 26.36 42.80 6.17 15.97 3.29 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
133 26 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 280.00 525.00 894.000 111.000 286.00 113.000 13.198 30.593 9.360 23.275 174.00 653.000 95.232 163.000 1.980 163.000 80.128 16.37 766.00 93.58 660.00 253.000 702.00 389 553 193.00 427.00 39.685 321.000 496.000 9.657 37.403 1784.000 157.00 665.00 295.00 2180.00 6363.00
134 26 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 209.00 380.00 836.000 74.333 164.00 60.004 8.996 20.619 71.052 23.994 124.00 456.000 58.649 117.000 1.980 209.000 71.003 17.17 459.00 118.00 531.00 192.000 679.00 459 769 168.00 407.00 51.595 250.000 307.000 8.411 70.721 1328.000 113.00 567.00 236.00 1572.00 3605.00
135 26 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 288.00 641.00 1098.000 122.000 139.00 111.000 14.282 48.154 80.298 48.404 230.00 657.000 85.433 180.000 1.980 217.000 124.000 41.99 680.00 136.00 787.00 257.000 715.00 735 832 188.00 584.00 73.519 363.000 526.000 11.500 67.799 1782.000 135.00 732.00 335.00 3573.00 6153.00
136 27 0 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 59881 8971 14.98 97.40 50.06 9.57 75.84 24.16 3.59 8.13 64.83 13.40 15.67 6.10 19.31 18.81 16.83 76.67 6.10 6.82 2.00 88.16 47.06 10.53 11.24 10.41 9.93 6.94 11.93 16.38 67.35 180.000 62.930 127.00 2.970 36.468 4.794 25.690 5.645 46.84 139.000 1.295 17.624 2.360 148.000 9.904 9.46 143.00 64.82 261.00 507.000 809.00 891 476 76.81 136.00 18.740 113.000 98.896 20.817 358.000 925.000 91.47 168.00 123.00 653.00 761.00
137 27 1 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 60193 23874 39.66 96.72 58.83 16.24 80.37 19.63 1.87 3.31 68.58 10.62 14.72 6.08 21.33 22.83 3.80 80.61 3.63 8.03 0.33 94.16 57.26 2.96 5.01 10.19 4.72 1.41 1.24 3.57 32.49 145.000 59.668 35.43 2.970 12.151 4.287 29.390 5.645 54.69 26.852 0.940 6.660 2.360 142.000 12.494 6.13 91.76 118.00 294.00 354.000 659.00 811 759 35.98 83.92 12.544 90.832 44.557 39.375 223.000 830.000 67.87 147.00 58.83 260.00 528.00
138 27 3 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 8.66 39.21 135.000 67.453 61.20 3.273 20.290 5.826 23.842 5.819 45.26 64.203 1.569 9.863 3.090 66.315 10.766 5.31 155.00 68.00 355.00 420.000 945.00 1063 603 56.22 136.00 16.032 94.647 49.105 19.468 230.000 804.000 72.71 206.00 83.10 435.00 814.00
139 27 5 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 135335 27834 20.57 97.35 44.75 5.61 63.22 36.78 8.49 17.63 39.01 23.16 30.72 7.11 17.53 10.38 12.16 42.70 4.93 19.28 4.68 67.37 4.10 26.64 17.76 19.21 14.54 14.47 10.60 2.18 38.51 215.000 76.341 16.50 2.320 10.862 2.224 8.932 1.520 53.96 36.506 1.920 7.437 1.980 105.000 8.874 7.63 77.70 68.16 389.00 204.000 723.00 1321 487 50.26 160.00 12.674 125.000 27.485 45.764 177.000 710.000 55.82 360.00 90.56 239.00 351.00
140 27 8 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 247113 62042 25.11 96.41 43.93 14.82 78.42 21.58 2.52 5.24 65.35 12.22 17.53 4.90 20.39 17.20 2.93 77.86 2.11 8.78 2.94 84.64 9.70 10.64 13.93 9.90 11.59 8.48 3.51 22.57 29.97 91.301 58.481 24.03 7.004 8.899 5.515 27.077 7.972 25.93 37.523 2.650 3.138 5.142 70.641 12.783 7.59 94.97 56.14 551.00 288.000 717.00 1003 373 39.51 130.00 9.800 75.273 27.655 7.838 145.000 524.000 52.18 137.00 41.89 251.00 338.00
141 27 14 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 142053 56983 40.11 93.01 62.72 12.87 85.10 14.90 0.69 1.64 68.02 8.40 21.03 2.55 17.03 13.68 1.08 83.33 0.37 7.38 0.26 79.00 16.07 3.01 5.85 5.16 5.24 1.77 0.70 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
142 27 21 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 60384 17029 28.20 93.65 40.96 21.36 83.80 16.20 1.47 2.32 63.52 9.69 23.80 2.99 23.55 15.58 0.91 78.16 1.09 8.13 1.00 83.65 17.72 4.70 8.42 5.99 7.22 3.76 1.56 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
143 28 0 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 47645 37377 78.45 94.61 2.47 9.41 78.56 17.02 1.71 4.63 34.94 16.09 44.77 4.21 4.35 28.99 59.42 67.26 3.10 26.22 7.50 72.82 53.90 13.50 28.20 3.04 7.70 7.67 0.96 9.61 35.14 22.350 20.460 3.79 22.660 3.480 3.530 9.020 3.210 9.75 7.060 1.210 1.550 0.770 20.320 2.980 2.78 20.67 13.18 290.00 37.840 754.00 653 382 20.62 170.00 9.210 42.210 9.160 3.660 54.980 131.000 33.28 78.76 74.92 178.00 1807.00
144 28 1 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 87452 43025 49.20 91.54 1.98 26.71 92.06 6.76 0.77 4.67 47.21 4.70 45.17 2.92 6.06 18.18 60.61 81.01 3.95 14.10 12.04 85.97 79.84 5.82 14.15 1.11 4.77 2.84 0.00 12.71 13.67 11.390 16.950 2.77 3.020 2.690 2.960 29.730 1.790 5.89 3.490 1.610 2.220 2.640 103.000 6.370 5.67 25.55 37.67 369.00 23.230 830.00 341 373 18.73 111.00 6.740 23.260 13.120 5.700 76.070 128.000 32.15 77.14 17.37 97.61 3031.00
145 28 3 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 124056 80873 65.19 95.41 0.37 14.36 90.47 5.98 0.39 0.60 41.84 4.26 51.08 2.81 18.82 35.29 43.53 86.97 0.10 11.32 1.10 75.49 12.12 3.80 14.94 0.24 0.88 3.25 0.00 37.44 26.01 28.120 25.360 9.28 13.970 5.470 5.150 38.770 3.780 11.03 16.650 3.350 5.760 5.140 32.780 7.800 5.95 60.09 24.86 338.00 39.760 682.00 2208 349 27.24 174.00 11.700 30.960 38.010 10.120 76.070 213.000 35.85 86.63 43.82 185.00 2088.00
146 28 5 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 27.69 16.65 26.730 24.380 9.95 7.920 2.820 4.000 58.110 1.790 8.23 7.760 1.750 3.200 3.390 44.050 4.860 3.77 51.92 25.90 506.00 38.490 451.00 1325 306 19.69 141.00 6.280 18.330 27.170 7.470 68.020 184.000 35.13 89.68 25.77 160.00 2691.00
147 28 8 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 87840 57990 66.02 95.60 0.11 15.80 89.00 9.49 1.56 5.02 45.89 7.18 43.54 3.39 6.10 19.51 56.71 84.23 3.14 9.95 10.70 72.13 52.73 7.06 17.53 0.74 3.13 5.13 0.13 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
148 29 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 28.88 11.37 38.621 18.644 2.81 9.882 3.700 4.484 10.147 2.017 4.37 16.099 1.929 2.217 2.373 22.448 5.366 2.67 56.84 19.09 121.00 165.000 278.00 2642 893 12.58 118.00 9.063 71.540 36.892 9.225 98.841 153.000 9.87 43.25 1.92 91.04 849.00
149 29 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 32.32 11.74 23.505 14.109 2.81 8.767 2.565 4.264 25.300 2.483 4.11 23.064 1.929 2.217 2.373 38.737 17.441 3.48 47.53 43.53 86.72 138.000 254.00 4090 934 12.58 156.00 9.063 38.049 32.360 7.475 132.000 173.000 9.37 53.25 1.92 97.03 1026.00
150 29 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 25.42 7.75 19.917 18.280 2.72 2.813 1.666 2.416 11.420 1.760 3.33 3.635 1.630 1.230 1.693 23.780 15.880 17.02 38.18 30.19 301.00 163.000 98.45 422 469 7.34 128.00 14.917 59.891 23.381 4.874 81.038 142.000 5.65 29.22 1.92 55.92 693.00
151 29 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 16.71 7.06 19.917 15.917 2.72 2.813 1.666 2.229 11.848 12.332 3.08 1.100 1.350 1.230 2.025 15.579 10.484 3.34 28.88 28.36 209.00 154.000 88.98 365 499 7.34 107.00 36.898 76.513 22.115 5.734 83.628 132.000 5.65 26.13 1.92 61.63 668.00
152 29 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 40.77 11.37 32.156 21.931 2.81 11.022 2.105 3.003 6.884 14.236 3.08 1.969 2.247 3.356 2.828 13.545 9.354 3.89 55.29 20.23 152.00 235.000 81.91 458 893 17.01 128.00 38.439 108.000 32.360 9.225 88.759 142.000 7.31 140.00 1.92 79.15 1019.00
153 30 0 49 younger Male Low 3 No NA DT HMII Alive alive 196315 122053 62.17 95.37 60.66 22.66 91.10 8.90 0.45 4.95 85.57 6.30 5.42 2.71 13.13 28.32 20.05 86.32 2.45 5.42 0.96 98.68 94.19 2.50 2.96 4.63 1.24 0.33 0.89 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
154 30 1 49 younger Male Low 3 No NA DT HMII Alive alive 91909 37622 40.93 96.10 43.22 38.85 95.48 4.52 0.14 2.19 85.07 3.09 10.33 1.51 21.03 27.23 9.08 88.84 1.13 3.91 0.23 98.77 88.27 1.16 1.16 2.34 0.38 0.21 0.45 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
155 30 5 49 younger Male Low 3 No NA DT HMII Alive alive 153762 86281 56.11 95.83 65.54 24.24 91.12 8.88 0.65 2.84 81.43 6.30 9.62 2.65 21.14 28.73 15.89 83.00 1.97 6.74 0.36 98.22 93.16 3.20 2.29 5.47 1.11 0.77 1.56 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
156 30 8 49 younger Male Low 3 No NA DT HMII Alive alive 82406 47485 57.62 92.71 65.75 17.46 91.79 8.21 1.08 1.99 76.35 5.70 15.41 2.54 20.56 26.95 11.99 78.67 1.35 7.97 0.28 97.55 85.62 2.65 2.56 5.19 1.18 0.86 1.79 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
157 31 0 62 older Male High 2 No NA BTT CMAG Died dead 105070 38521 36.66 98.39 47.84 12.88 60.82 39.18 11.08 2.89 33.87 29.43 26.68 10.01 19.72 21.92 2.99 59.35 14.64 20.23 3.45 81.40 46.10 24.47 24.27 24.04 11.00 17.02 1.51 8.54 60.57 363.000 41.314 133.00 3.271 11.758 2.224 42.519 17.512 99.72 208.000 1.950 1.450 2.382 76.080 15.538 10.70 41.79 124.00 480.00 240.000 583.00 1205 2213 43.09 202.00 15.656 88.621 44.444 4.619 159.000 99.662 8.24 246.00 0.39 1505.00 1842.00
158 31 1 62 older Male High 2 No NA BTT CMAG Died dead 162336 73080 45.02 99.08 49.35 13.95 74.24 25.76 7.77 3.84 45.21 19.33 28.85 6.61 19.16 23.45 0.81 65.34 14.40 13.50 3.17 86.68 71.39 23.26 17.70 15.21 6.93 13.29 0.88 17.15 153.00 437.000 50.974 202.00 14.614 15.550 2.802 77.669 27.576 127.00 346.000 2.504 2.393 2.558 388.000 22.806 20.90 59.99 150.00 433.00 182.000 771.00 1505 2390 54.32 262.00 13.926 102.000 68.086 7.153 313.000 169.000 9.39 373.00 0.39 2102.00 1842.00
159 31 3 62 older Male High 2 No NA BTT CMAG Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 21.40 85.72 386.000 52.666 141.00 13.237 8.380 3.206 207.000 23.994 108.00 256.000 3.104 4.254 3.299 844.000 34.327 22.27 70.68 205.00 540.00 278.000 454.00 1971 4894 52.04 226.00 9.089 95.130 74.895 9.657 206.000 230.000 12.58 318.00 19.30 1420.00 1900.00
160 31 5 62 older Male High 2 No NA BTT CMAG Died dead 111264 60487 54.36 99.15 45.94 16.13 73.16 26.84 4.64 1.82 52.04 20.07 20.94 6.95 22.93 23.47 0.50 77.11 9.30 9.58 1.20 88.92 81.82 22.20 19.01 16.98 6.89 13.57 1.36 6.39 80.87 363.000 54.635 155.00 11.208 6.816 1.861 71.052 30.424 100.00 233.000 1.920 1.450 2.382 42.999 22.377 14.33 34.41 116.00 363.00 200.000 364.00 1276 1849 38.67 198.00 7.295 92.379 47.259 9.966 195.000 136.000 2.89 326.00 19.30 1292.00 1072.00
161 31 8 62 older Male High 2 No NA BTT CMAG Died dead 165870 67935 40.96 98.88 43.01 15.86 70.23 29.77 7.85 1.88 40.39 20.49 29.66 9.46 22.70 29.00 1.88 68.61 9.60 16.11 1.47 89.57 69.00 20.12 16.59 17.10 7.02 12.40 2.06 8.54 55.67 299.000 54.916 168.00 10.543 4.146 2.224 51.707 60.924 80.71 211.000 2.314 2.393 3.299 24.277 15.538 9.59 56.39 99.84 439.00 210.000 312.00 968 1056 55.43 187.00 9.350 73.454 31.722 12.711 151.000 109.000 5.73 251.00 0.39 1247.00 1465.00
162 32 0 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 2.98 33.241 14.342 3.42 2.970 0.420 0.853 3.452 2.290 13.82 3.190 0.940 0.850 2.360 50.340 2.770 5.07 17.40 64.31 653.00 26.894 241.00 316 496 12.21 15.96 3.050 12.001 8.034 2.282 17.749 119.000 3.85 236.00 0.92 44.32 1793.00
163 32 1 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 24.67 100.000 10.761 20.24 2.970 8.281 0.853 136.000 3.964 19.25 3.688 0.940 1.090 2.360 130.000 11.629 6.13 45.48 139.00 541.00 88.075 437.00 264 768 20.97 77.56 5.231 40.568 66.481 1.717 408.000 245.000 55.44 141.00 16.95 74.91 10443.75
164 32 3 72 older Male Low 4 Yes NA BTT HMII Died dead 424375 21971 5.18 71.78 56.98 18.53 79.84 20.16 3.08 0.51 57.91 14.07 24.06 3.97 31.74 15.95 13.65 57.94 0.65 28.51 0.17 93.57 53.33 2.87 9.69 15.61 3.87 2.16 3.99 2.18 6.65 125.000 14.770 4.61 2.320 14.644 1.590 22.499 1.520 18.98 2.120 1.920 1.450 1.980 72.656 11.749 1.99 23.32 48.13 195.00 286.000 323.00 579 874 10.05 63.62 2.448 45.479 16.356 0.944 52.863 60.307 4.34 61.08 0.39 52.69 813.00
165 32 5 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 10.24 45.698 19.436 2.80 2.970 13.723 0.458 31.010 2.290 9.91 3.190 0.940 0.850 2.360 109.000 9.904 3.42 31.87 57.12 177.00 35.314 337.00 361 834 30.99 61.08 3.916 39.476 11.877 2.878 32.504 142.000 2.92 154.00 0.92 69.60 1160.00
166 32 8 72 older Male Low 4 Yes NA BTT HMII Died dead 177249 103442 58.36 76.40 15.43 5.64 65.13 34.87 5.21 0.20 35.56 27.91 31.90 4.62 26.60 8.28 13.24 67.89 0.29 20.13 0.00 78.60 22.22 5.50 16.09 25.56 6.80 4.73 0.89 2.18 2.98 74.914 45.496 2.31 2.320 4.146 1.590 63.920 1.520 21.62 2.120 20.861 1.450 1.980 773.000 19.378 33.60 45.46 368.00 630.00 220.000 265.00 523 1437 110.00 102.00 4.899 63.342 35.967 3.045 101.000 80.459 15.43 193.00 0.39 234.00 2752.00

Identification of clusters

We double standardized the data and biclustered it. We found 3 clusters of patients, and 3 clusters of B-cells.

require(pheatmap, quietly = T)
require(RColorBrewer, quietly = T)


colorfun <- function(nlevels, ...){
    if(nlevels > 10) return(standardColors(nlevels))
    if(nlevels <=10) return(pal_d3(...)(nlevels))
} 

annotation.row <- data.frame("Molecule" = factor(c("none", rep("B-cell",29), rep("cytokine",38))))
annotation.col <- df.raw[,c(1,4,5,6,7,8,9,10,11,12,13)]
annotation.colors <- lapply(colnames(annotation.col), function(nam){
    colrs <- colorfun(nlevels(annotation.col[[nam]]))
    names(colrs) <- levels(annotation.col[[nam]])
    return(colrs)
})
names(annotation.colors) <- colnames(annotation.col)
annotation.colors[["Molecule"]] <- colorfun(nlevels(annotation.row$Molecule))
names(annotation.colors[["Molecule"]]) <- levels(annotation.row$Molecule)

df.patient <- double_standardize(t(na.omit(df.raw[,c(3,bcellcyto)])))
rownames(annotation.row) <- rownames(df.patient)
pheatmap(df.patient, 
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
         breaks = c(seq(-max(abs(df.patient), na.rm=T), 0, length.out = 50), seq(0.001, max(abs(df.patient), na.rm=T), length.out = 50)),
         #scale = "row",
         fontsize = 7,
         cutree_rows = 4,
         cutree_cols = 4,
         cluster_cols = T,
         cluster_rows = T,
         annotation_col = annotation.col,
         annotation_row = annotation.row,
         annotation_colors = annotation.colors,
         clustering_method = "ward.D2"
)

Metadata associations

We computed the statistical associations between sample groups specified by the metadata.

Survival

We computed metadata factors that were statistically associated with survival.

df.factors <- df[match(levels(df$PatientID), df$PatientID),]
isfactor <- which(sapply(df.factors, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Survival","Outcome", "LowIntermacs"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Survival)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 4.900 0.7440 39.00 0.0974 0.681 RVAD
2 NA NA NA 0.354 0.823 Device Type
3 2.610 0.1070 188.00 0.569 0.823 Sensitized
4 NA NA NA 0.616 0.823 InterMACS
5 0.677 0.0853 5.94 0.674 0.823 Sex
6 1.480 0.2450 9.82 0.705 0.823 AgeGreater60
7 0.879 0.0656 7.89 1 1 VAD Indication

InterMACS

We computed metadata factors that were statistically associated with a binarized InterMACS score.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("LowIntermacs", "InterMACS"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$LowIntermacs)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 NA NA NA 0.135 0.983 Device Type
2 3.070 0.520 21.10 0.246 0.983 AgeGreater60
3 0.583 0.062 5.43 0.653 1 VAD Indication
4 NA NA NA 0.689 1 Outcome
5 1.830 0.288 14.60 0.689 1 RVAD
6 1.310 0.116 15.90 1 1 Sensitized
7 1.210 0.140 9.40 1 1 Sex
8 0.956 0.153 6.40 1 1 Survival

Sensitization

We computed metadata factors that were statistically associated with sensitization.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sensitized"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Sensitized)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 0.127 0.00192 1.99 0.119 1 Sex
2 2.610 0.10700 188.00 0.569 1 Survival
3 NA NA NA 0.569 1 Outcome
4 0.426 0.02530 5.03 0.608 1 RVAD
5 NA NA NA 0.843 1 Device Type
6 1.310 0.11600 15.90 1 1 LowIntermacs
7 1.230 0.10100 15.40 1 1 AgeGreater60
8 NA NA NA 1 1 InterMACS
9 0.000 0.00000 Inf 1 1 VAD Indication

Sex

We computed metadata factors that were statistically associated with sex.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Sex"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$Sex)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 0.127 0.00192 1.99 0.119 0.89 Sensitized
2 3.860 0.48600 49.80 0.198 0.89 AgeGreater60
3 NA NA NA 0.368 1 Outcome
4 NA NA NA 0.447 1 Device Type
5 0.677 0.08530 5.94 0.674 1 RVAD
6 0.677 0.08530 5.94 0.674 1 Survival
7 1.210 0.14000 9.40 1 1 LowIntermacs
8 NA NA NA 1 1 InterMACS
9 1.840 0.15200 103.00 1 1 VAD Indication

Age

We computed metadata factors that were statistically associated with age.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("AgeGreater60"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$AgeGreater60)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 0.228 0.0274 1.45 0.114 0.442 RVAD
2 NA NA NA 0.128 0.442 InterMACS
3 NA NA NA 0.159 0.442 Outcome
4 3.860 0.4860 49.80 0.198 0.442 Sex
5 3.070 0.5200 21.10 0.246 0.442 LowIntermacs
6 NA NA NA 0.35 0.525 Device Type
7 1.950 0.2230 25.90 0.655 0.794 VAD Indication
8 1.480 0.2450 9.82 0.705 0.794 Survival
9 1.230 0.1010 15.40 1 1 Sensitized

VAD Indication

We computed metadata factors that were statistically associated with VAD Indication.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("VAD Indication"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`VAD Indication`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = F, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 NA NA NA 0.000242 0.00217 Outcome
2 0.000 0.0000 1.34 0.0619 0.278 RVAD
3 0.583 0.0620 5.43 0.653 1 LowIntermacs
4 1.950 0.2230 25.90 0.655 1 AgeGreater60
5 NA NA NA 0.837 1 InterMACS
6 NA NA NA 0.877 1 Device Type
7 1.840 0.1520 103.00 1 1 Sex
8 0.000 0.0000 Inf 1 1 Sensitized
9 0.879 0.0656 7.89 1 1 Survival

RVAD

We computed metadata factors that were statistically associated with RVAD.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("RVAD"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$RVAD)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 NA NA NA 0.00868 0.0782 Device Type
2 0.000 0.0000 1.34 0.0619 0.205 VAD Indication
3 NA NA NA 0.0739 0.205 Outcome
4 4.900 0.7440 39.00 0.0974 0.205 Survival
5 0.228 0.0274 1.45 0.114 0.205 AgeGreater60
6 NA NA NA 0.327 0.49 InterMACS
7 0.426 0.0253 5.03 0.608 0.689 Sensitized
8 0.677 0.0853 5.94 0.674 0.689 Sex
9 1.830 0.2880 14.60 0.689 0.689 LowIntermacs

Device-type

We computed metadata factors that were statistically associated with Device Type.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[!(names(isfactor) %in% c("Device Type"))], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`Device Type`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
    ft$p
})

fisher.OR.0 <- lapply(contingency, function(this_table){
    ft <- fisher.test(this_table, simulate.p.value = T, B = 10000)
    if(is.null(ft$estimate)) {
        ft$estimate <- NA
        ft$conf.int <- c(NA,NA)
    }
    ret <- list(OR = ft$estimate, lowerCL = ft$conf.int[1], upperCL = ft$conf.int[2])
    return(ret)
})
fisher.OR <- as.data.frame(do.call(rbind, fisher.OR.0))
fisher.OR$pvalue <- fisher.p
fisher.OR$qvalue <- p.adjust(fisher.p, method = "BH")
fisher.all <- as.data.frame(sapply(fisher.OR, as.numeric))
rownames(fisher.all) <- rownames(fisher.OR)

qtable <- signif(fisher.all[order(fisher.all$pvalue),],3)

qtable %>%
    mutate(
        `Factor` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12)
OR lowerCL upperCL pvalue qvalue Factor
1 NA NA NA 0.0089 0.0801 RVAD
2 NA NA NA 0.139 0.531 LowIntermacs
3 NA NA NA 0.274 0.531 InterMACS
4 NA NA NA 0.346 0.531 Outcome
5 NA NA NA 0.352 0.531 AgeGreater60
6 NA NA NA 0.354 0.531 Survival
7 NA NA NA 0.443 0.569 Sex
8 NA NA NA 0.835 0.874 Sensitized
9 NA NA NA 0.874 0.874 VAD Indication

Visualization of metadata associations

We used the GGally package to visualize pairwise dependencies between sample groups, separating out survivors and non-survivors (indicated by color).

df.temp <- df.factors
colnames(df.temp) <- make.names(colnames(df), unique = T)
#invisible(
suppressWarnings(
    suppressMessages(
        suppressWarnings(ggpairs(df.temp, 
                #mapping = aes(color = Survival), 
                columns = colnames(df.temp)[c(3,5,6,7,8,9,10,11,12,13)]) +
            ggplot2::theme_grey(base_size = 7)
    )))

#)

Variability of MCS devices

PCA

Using Principal Component Analysis (PCA), we saw large variability between device types, compared to the variability within device types. We also saw large variability between individual patients. None of the other features were clearly separable.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

isna <- unique(unlist(apply(df[,c(bcellcyto)], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df[-isna, c(bcellcyto)]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plotvars <- names(df)[c(11,1:13)]
plots.pca <- mclapply(plotvars, function(this_var){
    this_groups <- df[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)
names(plots.pca) <- plotvars
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
    cat("  \n###", names(plots.pca)[ii], "\n")
    suppressWarnings(print(plots.pca[[ii]]))
    cat("  \n")
}

Device Type

PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

Fisher’s exact test

We analyzed the dependency of device types on the other discrete variables using Fisher’s exact test.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[names(isfactor) != "Device Type"], 
                      function(this_factor){
                          table(df.factors[,this_factor], df.factors$`Device Type`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    fisher.test(this_table, simulate.p.value = T, B = 10000)$p
})
fisher.q <- p.adjust(fisher.p, method = "BH")

signif.ix <- which(fisher.p < 0.05)
signif.order <- sort(fisher.q[signif.ix], index.return = T)$ix
for(this_ix in signif.ix[signif.order]){
    cat("  \n###", names(contingency)[this_ix], "\n")
    cat(paste0("Benjamini-Hochberg qvalue = ", signif(fisher.q[this_ix], 2)),
        ". \n")
    print(kable(contingency[this_ix][[1]],  row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
}

RVAD

Benjamini-Hochberg qvalue = 0.077 .
HMII CMAG HVAD PVAD TAH
No 15 1 2 0 0
Yes 5 0 0 3 2

One-way repeated measures anova

We first analyzed the differences in B-cell levels across device types using a one-way repeated measures anova. Here we report any variables that had a statistically significant variance (\(p<0.05\)) across devices, time, or their interaction.

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[c(bcellcyto)]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[c(bcellcyto)]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- colnames(df)[c(bcellcyto)]
colnames(pvals) <- c("pvalue")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[c(bcellcyto)]
colnames(qBH) <- c("qvalue")

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)

sig.qtable <- cbind(pvals,qBH)[sigvars,,drop=F][order(apply(pvals[sigvars,,drop=F], 1, min)),,drop=F]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
        qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")
Significant device-type q-values
pvalue qvalue B-cell
1 1.2e-23 8e-22 IL-4
2 1.7e-16 5.8e-15 IL-1RA
3 0.00013 0.003 MCP-1
4 0.011 0.18 CD27-IgD+ mature naive
5 0.023 0.27 MIP-1a
6 0.026 0.27 IL-6
7 0.029 0.27 IL-8
8 0.045 0.35 CD19+CD5+

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the fit. Note that the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH 220.662 21.04556 96.0249 10.48497 0

IL-1RA

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 1331.313 152.1179 15.06184 8.751848 3e-07

MCP-1

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 1865.109 407.1979 16.04335 4.580351 0.000306

CD27-IgD+ mature naive

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD -33.85486 10.06687 23.54822 -3.362998 0.0026287

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
Device.TypeHVAD 32.60928 12.418797 15.28209 2.625801 0.0188723
Device.TypePVAD 16.86374 7.943291 17.68110 2.123017 0.0481402

IL-6

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 189.3771 68.51606 12.66841 2.763981 0.0164291

IL-8

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 77.25016 33.74592 15.23866 2.289171 0.0367422
Device.TypeTAH 73.38816 33.74592 15.23866 2.174727 0.0457932

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD 23.16366 8.615765 23.18582 2.688521 0.0130627

Linear mixed-effect model

We next analyzed the differences in B-cell levels across device types using a linear mixed effect model with time as a continuous variable, and included the interaction term. Here we report variables that had a statistically significant variance (Benjamini-Hochberg \(p<0.05\)) across devices, time, or their interaction.

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type * Time + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "Time", "Device:Time")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
        `Device:Time` = cell_spec(`Device:Time`, color = ifelse(qtable$`Device:Time` > 0.05, "grey", "red")),
        `Time` = cell_spec(`Time`, color = ifelse(qtable$`Time` > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")
Significant device-type q-values
Device Time Device:Time B-cell
1 8.9e-76 0.0033 1e-40 IL-4
2 1.1e-14 1 0.22 IL-1RA
3 0.78 0.0013 0.0041 CD19+CD11b+
4 0.22 0.98 0.0023 MIP-1a
5 0.0033 1 0.57 MCP-1
6 0.98 0.019 1 CD19+CD5+CD11b+
7 0.64 0.023 1 CD268 of +27-38++transitional
8 0.48 0.089 1 CD19+CD268+
9 1 0.089 0.78 IL-1b
10 1 0.095 0.98 CD19+27-38+CD5+transitionals
11 1 0.12 1 CD19 of live lymph
12 0.57 0.31 0.12 CD27+38++plasma blasts
13 0.19 0.24 0.3 CD27-IgD+ mature naive
14 0.57 0.22 1 CD27+IgD- switched memory
15 0.22 1 0.97 IL-6
16 0.34 0.22 1 CD19+CD5+
17 1 0.22 1 CD19+CD5+CD24hi
18 1 0.22 1 IL-3
19 1 0.22 0.76 sCD40L
20 0.98 0.23 1 num lymph
21 0.23 1 1 IL-8

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:Time 57.66099 4.0869 90.99768 14.10873 0

IL-1RA

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 1583.30519 170.53857 23.97074 9.284147 0.0000000
Device.TypeCMAG:Time -74.12118 22.72778 71.75398 -3.261260 0.0016987

CD19+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:Time 2.1101624 0.5538816 89.91926 3.809771 0.0002541
Device.TypePVAD:Time 0.8054722 0.2943289 88.29842 2.736640 0.0075041
Time 0.2628129 0.1188074 89.80589 2.212092 0.0294973

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
Device.TypeHVAD:Time 15.08983 3.068267 74.37332 4.918031 5.1e-06

MCP-1

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 2455.3824 490.26835 32.93458 5.008242 0.0000181
Device.TypeCMAG:Time -173.6232 81.38512 73.35568 -2.133353 0.0362406

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Time 0.290629 0.1150892 91.27483 2.525249 0.0132869

CD268 of +27-38++transitional

Estimate Std. Error df t value Pr(>|t|)
Time -0.7873216 0.3030382 89.79933 -2.598094 0.0109556

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
Time -0.6425061 0.230173 88.33548 -2.791405 0.0064310
Device.TypePVAD -37.7748686 16.138544 27.53030 -2.340662 0.0267323

IL-1b

Estimate Std. Error df t value Pr(>|t|)
Time 0.3273811 0.1608446 72.42554 2.035387 0.0454716

CD19+27-38+CD5+transitionals

Estimate Std. Error df t value Pr(>|t|)
Time 0.0910247 0.0395736 89.94149 2.30014 0.0237538

CD19 of live lymph

Estimate Std. Error df t value Pr(>|t|)
Time -0.3683497 0.140373 93.29085 -2.624078 0.0101509

CD27+38++plasma blasts

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD:Time 0.2768179 0.1033923 88.20166 2.677355 0.0088487

CD27-IgD+ mature naive

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD -32.958219 10.7368535 29.72853 -3.069635 0.0045463
Device.TypeTAH:Time -2.247904 0.8166692 89.34041 -2.752527 0.0071618

CD27+IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)
Time 0.3654018 0.1606941 88.62458 2.273897 0.0253875
Device.TypePVAD 25.0335542 10.8348873 28.15496 2.310458 0.0284029

IL-6

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 304.8154 95.59353 46.0739 3.188661 0.0025701

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD 19.97858 9.433345 33.29569 2.117868 0.0417364

CD19+CD5+CD24hi

Estimate Std. Error df t value Pr(>|t|)

IL-3

Estimate Std. Error df t value Pr(>|t|)
Time 0.2062222 0.0939434 74.80342 2.195175 0.0312533

sCD40L

Estimate Std. Error df t value Pr(>|t|)

IL-8

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 96.94986 42.04762 34.74735 2.305716 0.0272161

Two-way repeated measures anova

Finally, we attempted to analyze the differences in B-cell levels across device types using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across devices, times, or their interaction. As there were 5 device types and 7 timepoints, but only 166 samples, this model is severely underpowered. The posterior belief in any of these results should therefore be quite small (as a consequence of Bayes rule).

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[bcellcyto]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type * factor(Time) + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[bcellcyto]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "factor(Time)", "Device:factor(Time)")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[bcellcyto]
colnames(qBH) <- colnames(pvals)

sigvars <- apply(apply(pvals, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
        `Device:factor(Time)` = cell_spec(`Device:factor(Time)`, color = ifelse(qtable$`Device:factor(Time)` > 0.05, "grey", "red")),
        `factor(Time)` = cell_spec(`factor(Time)`, color = ifelse(qtable$`factor(Time)` > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%")
Significant device-type q-values
Device factor(Time) Device:factor(Time) B-cell
1 1.1e-81 3.3e-07 1.4e-123 IL-4
2 1.1e-14 0.55 0.022 IL-1RA
3 0.2 0.09 1.7e-10 CD19+CD5+
4 0.13 0.79 2.5e-09 MIP-1a
5 0.19 0.47 3.5e-06 IL-6
6 0.42 0.046 5e-06 CD27+IgD- switched memory
7 0.66 0.093 7.2e-05 CD19+CD27+
8 0.66 0.38 7.2e-05 CD27-IgD- switched memory
9 0.0017 0.26 2e-04 MCP-1
10 0.69 0.00038 0.0017 CD19+CD11b+
11 0.092 0.19 0.0037 CD27-IgD+ mature naive
12 1 0.0063 1 CD19 of live lymph
13 0.68 0.0063 1 G-CSF
14 0.19 0.012 1 IL-8
15 1 1 0.013 CD19CD24hiCD38-memory
16 1 0.04 1 IL-10
17 0.32 0.046 1 CD27+IgD-IgM+ switched memory
18 1 0.053 1 IL-1b
19 1 1 0.063 CD19+24dim38dim naive mature
20 1 0.66 0.08 CD19+CD27+CD24hi
21 0.66 0.083 1 CD268 of +27-38++transitional
22 0.96 0.14 0.7 CD19+CD5+CD11b+
23 0.37 0.19 0.15 CD19+CD268+
24 1 0.16 1 Eotaxin
25 1 0.19 0.97 MDC
26 0.53 0.26 0.21 CD27+38++plasma blasts
27 0.26 0.21 0.94 IL-15
28 0.23 0.35 1 TNF-b
29 1 0.23 1 IL-5

We plotted mean levels across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit. Note that the reference level for the time comparisons is timepoint 0, and the reference level for the devices is the HeartMate-II (HMII).

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
        if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
        sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),], row.names = T) %>%
                  kable_styling(bootstrap_options = c("striped", 
                                                      "hover", 
                                                      "condensed",
                                                      "responsive"),
                                font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

IL-4

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 473.44988 23.37471 56.29429 20.254795 0.0000000
Device.TypeTAH:factor(Time)8 398.35765 23.34425 56.24413 17.064486 0.0000000
Device.TypeTAH:factor(Time)3 220.44490 23.34425 56.24413 9.443220 0.0000000
Device.TypeTAH:factor(Time)1 53.48854 23.37471 56.29429 2.288309 0.0258953

IL-1RA

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG 1277.2905 193.4375 37.65943 6.603117 0.0000001
Device.TypeCMAG:factor(Time)1 693.5273 189.4589 57.30055 3.660569 0.0005499
Device.TypePVAD:factor(Time)5 -268.5598 130.7894 58.77891 -2.053375 0.0444945

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 79.25674 11.074689 67.92417 7.156566 0.0000000
Device.TypePVAD 35.52841 9.894557 43.53672 3.590703 0.0008326
Device.TypePVAD:factor(Time)3 -25.18076 8.751886 71.43189 -2.877181 0.0052872
Device.TypePVAD:factor(Time)5 -31.58267 11.241069 73.06155 -2.809579 0.0063611
Device.TypePVAD:factor(Time)8 -20.60508 7.572762 69.16235 -2.720946 0.0082276

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
Device.TypeHVAD:factor(Time)8 159.25574 23.02507 59.36219 6.916624 0.0000000
Device.TypePVAD:factor(Time)3 46.08958 15.73936 61.98365 2.928301 0.0047623
Device.TypeHVAD:factor(Time)1 47.74151 23.05589 59.40146 2.070686 0.0427425

IL-6

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG:factor(Time)3 798.6437 139.2084 56.45800 5.737037 0.0000004
Device.TypeTAH:factor(Time)1 310.2112 139.3936 56.50358 2.225434 0.0300595
Device.TypeCMAG:factor(Time)1 291.1312 139.3936 56.50358 2.088556 0.0412650

CD27+IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD:factor(Time)3 -44.11347 8.159997 70.32571 -5.406064 0.0000008
Device.TypePVAD 46.55839 10.997051 35.92275 4.233716 0.0001521
Device.TypePVAD:factor(Time)8 -25.13824 7.037695 68.87484 -3.571943 0.0006518
Device.TypePVAD:factor(Time)5 -35.76427 10.505189 71.34328 -3.404439 0.0010914
Device.TypeHVAD:factor(Time)1 22.95446 9.340788 68.84096 2.457443 0.0165160
Device.TypeTAH:factor(Time)1 19.80313 9.340788 68.84096 2.120071 0.0376068

CD19+CD27+

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD:factor(Time)3 -44.86147 9.352169 70.81526 -4.796905 0.0000087
Device.TypePVAD 40.90474 11.677321 38.82944 3.502922 0.0011749
Device.TypePVAD:factor(Time)5 -36.38395 12.029880 72.05224 -3.024465 0.0034501
Device.TypePVAD:factor(Time)8 -22.52547 8.075489 69.06666 -2.789363 0.0068193
Device.TypeHVAD:factor(Time)1 26.28504 10.718455 69.03750 2.452316 0.0167261

CD27-IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 71.68679 13.81377 66.76113 5.189516 0.0000021
Device.TypeTAH:factor(Time)14 34.72518 15.34586 76.65481 2.262837 0.0264767
Device.TypePVAD:factor(Time)3 -22.85502 10.65984 75.69366 -2.144030 0.0352412

MCP-1

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG:factor(Time)3 2642.6492 606.3312 58.57383 4.358425 0.0000536
Device.TypeCMAG 1709.5677 556.6078 48.15330 3.071405 0.0034988
factor(Time)1 363.0153 155.7998 59.46423 2.330012 0.0232187
Device.TypeCMAG:factor(Time)8 -1265.9221 606.3312 58.57383 -2.087839 0.0411707

CD19+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)14 52.95791 10.085405 72.02647 5.250946 0.0000015
Device.TypeTAH:factor(Time)1 21.89638 8.043661 70.05708 2.722190 0.0081767
Device.TypePVAD:factor(Time)14 17.80531 7.197456 72.09567 2.473834 0.0157228
Device.TypeTAH:factor(Time)3 16.76692 8.048562 70.13505 2.083219 0.0408780

CD27-IgD+ mature naive

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 -49.338009 12.632153 68.31654 -3.905748 0.0002179
Device.TypePVAD -40.588774 11.742845 41.95254 -3.456469 0.0012664
Device.TypeTAH:factor(Time)14 -42.518754 14.393799 71.18903 -2.953963 0.0042493
Device.TypeTAH:factor(Time)1 -29.588950 11.470396 69.40469 -2.579593 0.0120122
factor(Time)8 -7.421838 2.996063 68.79574 -2.477197 0.0157031
Device.TypeHVAD:factor(Time)1 -23.895494 11.470396 69.40469 -2.083232 0.0409155

CD19 of live lymph

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 8.670607 2.838543 69.69727 3.054598 0.0031913
Device.TypeHVAD:factor(Time)5 18.621453 8.106642 68.64255 2.297061 0.0246707
Device.TypeHVAD:factor(Time)3 16.313101 7.998551 68.60745 2.039507 0.0452529

G-CSF

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 75.26281 30.59711 60.67210 2.459801 0.0167667
Device.TypeTAH:factor(Time)1 251.33419 119.55029 59.29771 2.102330 0.0397798

IL-8

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 48.21632 15.8183 59.67957 3.048135 0.0034288

CD19CD24hiCD38-memory

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 -22.909686 6.066396 68.15378 -3.776490 0.0003363
Device.TypeHVAD:factor(Time)1 12.942595 5.519995 68.69011 2.344675 0.0219407
factor(Time)1 -3.388861 1.568014 68.34505 -2.161244 0.0341830

IL-10

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 162.0198 59.75738 58.42885 2.711294 0.0087864

CD27+IgD-IgM+ switched memory

Estimate Std. Error df t value Pr(>|t|)
factor(Time)5 10.711338 3.308708 71.05852 3.237317 0.0018346
factor(Time)3 8.467757 3.003200 71.42371 2.819578 0.0062189
factor(Time)14 8.273763 3.705650 70.67032 2.232742 0.0287354

IL-1b

Estimate Std. Error df t value Pr(>|t|)

CD19+24dim38dim naive mature

Estimate Std. Error df t value Pr(>|t|)
Device.TypePVAD:factor(Time)3 -51.79949 13.95422 76.12811 -3.712101 0.0003892

CD19+CD27+CD24hi

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 -22.54139 9.215036 68.32804 -2.446154 0.0170182
factor(Time)8 4.88642 2.184243 68.95940 2.237123 0.0285135
Device.TypeHVAD:factor(Time)1 18.31135 8.355636 69.79004 2.191497 0.0317555

CD268 of +27-38++transitional

Estimate Std. Error df t value Pr(>|t|)
factor(Time)8 -13.21005 5.828370 69.05531 -2.266508 0.0265563
factor(Time)14 -16.08481 7.874549 69.65371 -2.042632 0.0448754

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)5 -66.88010 17.16044 67.82056 -3.897342 0.0002253
Device.TypePVAD -39.35919 17.41639 37.46863 -2.259893 0.0297274

Eotaxin

Estimate Std. Error df t value Pr(>|t|)

MDC

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 -116.6333 46.70457 59.46922 -2.497257 0.0153028

CD27+38++plasma blasts

Estimate Std. Error df t value Pr(>|t|)
Device.TypeTAH:factor(Time)1 7.049114 2.979632 70.86460 2.365767 0.0207319
Device.TypeCMAG 7.332777 3.630995 44.46773 2.019495 0.0494876

IL-15

Estimate Std. Error df t value Pr(>|t|)
Device.TypeCMAG:factor(Time)3 15.986654 6.525592 58.14190 2.44984 0.0173241
factor(Time)8 3.382095 1.641873 58.49596 2.05990 0.0438665

TNF-b

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 -48.00227 17.12941 58.05021 -2.802330 0.0068843
factor(Time)3 -45.91700 16.76077 57.61190 -2.739553 0.0081753
factor(Time)5 -34.81589 17.12941 58.05021 -2.032521 0.0466840

IL-5

Estimate Std. Error df t value Pr(>|t|)
factor(Time)1 -8.197292 2.673786 59.39955 -3.065800 0.0032645
factor(Time)3 -6.821442 2.618986 58.78438 -2.604612 0.0116301
factor(Time)8 -6.460310 2.618986 58.78438 -2.466721 0.0165701
factor(Time)5 -6.183556 2.673786 59.39955 -2.312659 0.0242238

HeartMate-II analysis

The HeartMate-II (HMII) recipients were the largest group, and we analyzed them by themselves due to the previously observed variability across devices.

require(reshape2, quietly = T)
df.HMII <- subset(df, df$`Device Type`=="HMII")
df.long <- melt(df.HMII, id.vars = colnames(df)[1:13])

groups <- make.names(c("AgeGreater60", 
                       "Sex",
                       "LowIntermacs",
                       "RVAD", 
                       "Sensitized",
                       "VAD Indication", 
                       #"Device Type", 
                       "Survival",
                       "Outcome"))

names(df.long) <- make.names(names(df.long))

plots.ts <- mclapply(unique(df.long$variable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            scale_color_aaas() + scale_fill_aaas() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- unique(df.long$variable)

# for(ii in c(1:length(plots.ts))){
#     for(jj in 1:length(plots.ts[[ii]])){
#         suppressWarnings(print(plots.ts[[ii]][[jj]]))
#     }
# } 

PCA

Using PCA, we found large variability between individual patients, compared to the variability within individual patients. None of the other features were clearly separable.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

# Efron's double standardization
double_standardize <- function(x, niter = 100) {
    for(i in 1:niter) x <- t(scale(t(scale(x))))
    return(as.data.frame(x))
}

isna <- unique(unlist(apply(df.HMII[,bcellcyto], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df.HMII[-isna, bcellcyto]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plots.pca <- mclapply(names(df.HMII)[1:13], function(this_var){
    this_groups <- df.HMII[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)

names(plots.pca) <- names(df.HMII)[1:13]
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
    cat("  \n###", names(plots.pca)[ii], "\n")
    suppressWarnings(print(plots.pca[[ii]]))
    cat("  \n")
}

PatientID

Time

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Outcome

Survival

One-way repeated measures anova

We analyzed the differences in B-cell levels for various features using a one-way repeated measures anova. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")
Significant results
B-cell parameter qvalue
163 CD19+27+IgD-38++IgG ASC LowIntermacs 0.208
167 TNF-a LowIntermacs 0.266
456 MCP-1 Outcome 0.267
70 lymph Sex 0.270
49 IL-8 AgeGreater60 0.271
46 IL-15 AgeGreater60 0.271
2 num lymph AgeGreater60 0.299
3 lymph AgeGreater60 0.367
523 MCP-1 Survival 0.371
416 CD27+IgD+ unswitched memory Outcome 0.395
396 G-CSF VAD.Indication 0.483
83 CD27+IgD+IgM+ nonswitched memory Sex 0.505
33 TNF-a AgeGreater60 0.506
415 CD27-IgD- switched memory Outcome 0.507
51 Eotaxin AgeGreater60 0.521
215 CD27+IgD+ unswitched memory RVAD 0.525
29 CD19+27+IgD-38++IgG ASC AgeGreater60 0.527
20 CD19CD24hiCD38-memory AgeGreater60 0.530
137 lymph LowIntermacs 0.530
93 CD19+CD27+CD24hi Sex 0.532
118 Eotaxin Sex 0.533
418 CD27+IgD+IgM+ nonswitched memory Outcome 0.536
116 IL-8 Sex 0.536

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above one-way anova. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 2.078718 0.60011 17.90374 3.463894 0.0027878

TNF-a

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 18.20785 6.096687 14.89374 2.986515 0.0092805

MCP-1

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied 498.6263 225.4061 12.04031 2.212125 0.0470293

lymph

Estimate Std. Error df t value Pr(>|t|)
SexMale -18.44662 6.370603 17.92503 -2.895584 0.0096664

IL-8

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 44.84973 15.53423 14.72595 2.887155 0.0114543

IL-15

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 6.463922 2.256074 13.72298 2.86512 0.0126788

num lymph

lymph

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older -15.22801 5.790664 17.53478 -2.629751 0.0172536

MCP-1

Estimate Std. Error df t value Pr(>|t|)
Survivaldead 466.6129 178.0397 14.27031 2.620836 0.0198971

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT 16.53063 6.610333 15.90681 2.500726 0.0237124

G-CSF

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT -71.81929 31.45987 13.6818 -2.282886 0.0389804

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
SexMale -16.26667 7.449772 17.76457 -2.183512 0.0426655

TNF-a

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 15.47275 7.106515 15.54261 2.177263 0.045244

CD27-IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)

Eotaxin

Estimate Std. Error df t value Pr(>|t|)

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)

CD19CD24hiCD38-memory

Estimate Std. Error df t value Pr(>|t|)

lymph

Estimate Std. Error df t value Pr(>|t|)

CD19+CD27+CD24hi

Estimate Std. Error df t value Pr(>|t|)

Eotaxin

Estimate Std. Error df t value Pr(>|t|)

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)

IL-8

Estimate Std. Error df t value Pr(>|t|)

Linear mixed-effect model

We analyzed the differences in B-cell levels for various features using a linear mixed effect model, with time as a continuous variable. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " * Time + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")
Significant results
B-cell parameter qvalue
885 CD27+IgD+ unswitched memory Outcome:Time 0.000
893 CD19+CD268+ Outcome:Time 0.000
297 CD19+27+IgD-38++IgG ASC LowIntermacs 0.033
790 IP-10 VAD.Indication:Time 0.035
69 num lymph AgeGreater60:Time 0.038
514 IL-6 RVAD:Time 0.053
2 num lymph AgeGreater60 0.060
765 CD19+CD5+CD11b+ VAD.Indication:Time 0.069
770 TNF-a VAD.Indication:Time 0.077
858 MCP-1 Outcome 0.192
301 TNF-a LowIntermacs 0.193
219 CD19+24dim38dim naive mature Sex:Time 0.208
49 IL-8 AgeGreater60 0.229
137 lymph Sex 0.233
46 IL-15 AgeGreater60 0.237
896 CD19+CD5+ Outcome:Time 0.264
383 IFN-a2 LowIntermacs:Time 0.267
899 CD19+CD5+CD11b+ Outcome:Time 0.280
762 CD19+CD5+ VAD.Indication:Time 0.289
345 CD27-38++ transitional LowIntermacs:Time 0.298
97 IL-12(p40) AgeGreater60:Time 0.300
761 CD19+CD11b+ VAD.Indication:Time 0.321
992 MCP-1 Survival 0.324
904 TNF-a Outcome:Time 0.340
3 lymph AgeGreater60 0.354
818 CD27+IgD+ unswitched memory Outcome 0.356
764 CD19+CD5+CD24hi VAD.Indication:Time 0.362
887 CD27+IgD+IgM+ nonswitched memory Outcome:Time 0.363
1050 IL-6 Survival:Time 0.367
268 sCD40L Sex:Time 0.369
91 CD19+CD11b+ AgeGreater60:Time 0.384
123 Fractalkine AgeGreater60:Time 0.394
377 IL-1b LowIntermacs:Time 0.409
1049 IL-3 Survival:Time 0.415
115 IFN-a2 AgeGreater60:Time 0.416
791 MCP-1 VAD.Indication:Time 0.437
68 num Total PBMC AgeGreater60:Time 0.441
380 IL-6 LowIntermacs:Time 0.464
731 G-CSF VAD.Indication 0.465
77 CD27-38++ transitional AgeGreater60:Time 0.466
1010 CD3 of live lymph Survival:Time 0.475
150 CD27+IgD+IgM+ nonswitched memory Sex 0.476
518 IL-8 RVAD:Time 0.476
898 CD19+CD5+CD24hi Outcome:Time 0.479
252 Eotaxin Sex:Time 0.480
523 MCP-1 RVAD:Time 0.487
382 TGF-a LowIntermacs:Time 0.489
206 CD3 of live lymph Sex:Time 0.492
33 TNF-a AgeGreater60 0.493
234 TNF-a Sex:Time 0.493
783 IL-15 VAD.Indication:Time 0.494
211 CD27-38++ transitional Sex:Time 0.495
817 CD27-IgD- switched memory Outcome 0.496
416 CD27+IgD+ unswitched memory RVAD 0.496
794 MIP-1a VAD.Indication:Time 0.505
51 Eotaxin AgeGreater60 0.508
924 IP-10 Outcome:Time 0.508
271 lymph LowIntermacs 0.513
95 CD19+CD5+CD11b+ AgeGreater60:Time 0.522
126 GM-CSF AgeGreater60:Time 0.527
128 G-CSF AgeGreater60:Time 0.529
20 CD19CD24hiCD38-memory AgeGreater60 0.529
29 CD19+27+IgD-38++IgG ASC AgeGreater60 0.531
895 CD19+CD11b+ Outcome:Time 0.531
746 CD27+38++plasma blasts VAD.Indication:Time 0.531
185 Eotaxin Sex 0.532
820 CD27+IgD+IgM+ nonswitched memory Outcome 0.533
160 CD19+CD27+CD24hi Sex 0.533
391 Fractalkine LowIntermacs:Time 0.535
739 num lymph VAD.Indication:Time 0.536
371 IL-5 LowIntermacs:Time 0.538
90 CD268 of +27-38++transitional AgeGreater60:Time 0.539
183 IL-8 Sex 0.540
664 G-CSF Sensitized:Time 0.540
1027 CD19+CD268+ Survival:Time 0.540
516 TGF-a RVAD:Time 0.545
386 Eotaxin LowIntermacs:Time 0.545

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:Time -2.667973 0.494540 62.02792 -5.394858 0.0000011
OutcomeDied post OHT 23.798615 6.761184 17.90907 3.519888 0.0024617

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:Time -6.8213719 1.5139567 62.00943 -4.505659 0.0000299
Time -1.2473123 0.3522343 62.49855 -3.541144 0.0007592
OutcomeAlive s/p OHT:Time 0.9383259 0.4056434 62.45638 2.313179 0.0240193

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 3.058696 0.774233 45.35361 3.950614 0.0002697

IP-10

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time -126.82887 33.85589 54.58765 -3.746139 0.0004345
Time 60.64459 17.34161 54.85011 3.497056 0.0009404

num lymph

Estimate Std. Error df t value Pr(>|t|)
Time 8206.115 1791.316 1287.688 4.581054 0.0000051
AgeGreater60older:Time -8800.327 2381.169 2274.895 -3.695802 0.0002244

IL-6

Estimate Std. Error df t value Pr(>|t|)
RVADYes:Time 32.60694 9.175249 54.21954 3.553793 0.0007958

num lymph

Estimate Std. Error df t value Pr(>|t|)
Time 8206.115 1791.316 1287.688 4.581054 0.0000051
AgeGreater60older:Time -8800.327 2381.169 2274.895 -3.695802 0.0002244

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time 0.7535101 0.2208725 66.53183 3.411516 0.0011029

TNF-a

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time -5.629434 1.680142 55.89628 -3.350570 0.0014504
VAD.IndicationDT 23.272245 10.340359 28.83495 2.250622 0.0322084

MCP-1

Estimate Std. Error df t value Pr(>|t|)

TNF-a

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 23.97608 8.347092 41.27354 2.872387 0.0064058

CD19+24dim38dim naive mature

Estimate Std. Error df t value Pr(>|t|)
Time -0.8161430 0.2628953 64.83725 -3.104441 0.0028253
SexMale:Time 0.9045098 0.3076620 64.86966 2.939946 0.0045445

IL-8

Estimate Std. Error df t value Pr(>|t|)

lymph

Estimate Std. Error df t value Pr(>|t|)
SexMale -17.61531 7.156886 25.50525 -2.46131 0.0209393

IL-15

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 7.790226 2.627262 23.91797 2.96515 0.0067556

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
Time 0.8658199 0.2549408 64.48772 3.396161 0.0011744
OutcomeDied post OHT:Time -3.3958199 1.1020936 62.63883 -3.081244 0.0030615
OutcomeAlive s/p OHT:Time -0.6785876 0.2937450 64.32566 -2.310125 0.0241003

IFN-a2

Estimate Std. Error df t value Pr(>|t|)
Time 7.094916 2.400303 55.17787 2.955842 0.0045797
LowIntermacsHigh:Time -8.250956 2.967875 54.85053 -2.780089 0.0074308

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Time 0.9291898 0.2104973 64.10073 4.414260 0.0000397
OutcomeAlive s/p OHT:Time -0.8534246 0.2425036 63.97151 -3.519225 0.0008029

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time 0.7495301 0.2761314 67.17207 2.714397 0.0084299

CD27-38++ transitional

Estimate Std. Error df t value Pr(>|t|)
Time 0.2728189 0.0943080 68.33055 2.892849 0.0051174
LowIntermacsHigh:Time -0.3029055 0.1126997 67.21515 -2.687721 0.0090598

IL-12(p40)

Estimate Std. Error df t value Pr(>|t|)
Time 4.488736 1.571299 56.15354 2.856703 0.0059916
AgeGreater60older:Time -4.915360 1.833222 55.84263 -2.681268 0.0096251

CD19+CD11b+

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time 0.5911116 0.2251629 65.39615 2.625262 0.0107693

MCP-1

Estimate Std. Error df t value Pr(>|t|)
Survivaldead 500.4575 220.1017 30.55848 2.273755 0.0301634

TNF-a

Estimate Std. Error df t value Pr(>|t|)
Time -5.527423 1.705528 53.73771 -3.240887 0.0020479
OutcomeAlive s/p OHT:Time 6.230423 2.030162 53.98616 3.068929 0.0033572
OutcomeDied:Time 6.757500 2.229274 53.60892 3.031256 0.0037457
OutcomeDied -32.035464 13.772117 24.37281 -2.326110 0.0286425
OutcomeAlive s/p OHT -28.339350 12.411581 25.62297 -2.283299 0.0309577

lymph

Estimate Std. Error df t value Pr(>|t|)
Time 0.7428399 0.3610038 69.66204 2.057706 0.0433625

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:Time -2.667973 0.494540 62.02792 -5.394858 0.0000011
OutcomeDied post OHT 23.798615 6.761184 17.90907 3.519888 0.0024617

CD19+CD5+CD24hi

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time 0.4514841 0.1801168 68.30031 2.506618 0.0145744

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:Time -4.960664 1.778346 62.05844 -2.789481 0.0070021
OutcomeDied post OHT 42.763036 16.441772 20.60956 2.600878 0.0168391

IL-6

Estimate Std. Error df t value Pr(>|t|)
Survivaldead:Time 20.49333 8.218759 54.28222 2.493482 0.0157287

sCD40L

Estimate Std. Error df t value Pr(>|t|)
Time 564.2348 192.6758 56.44318 2.928415 0.0049057
SexMale:Time -561.8438 225.8697 56.13655 -2.487468 0.0158624

CD19+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Time 0.5499941 0.1571744 65.94446 3.499260 0.0008412
AgeGreater60older:Time -0.5051776 0.2059164 65.50984 -2.453314 0.0168239

Fractalkine

Estimate Std. Error df t value Pr(>|t|)
Time 12.02533 4.519847 54.65411 2.660561 0.0102192
AgeGreater60older:Time -12.81993 5.273092 54.32486 -2.431197 0.0183777

IL-1b

Estimate Std. Error df t value Pr(>|t|)
Time 0.8271702 0.2556659 53.88754 3.235356 0.0020785
LowIntermacsHigh:Time -0.7446451 0.3108987 53.73054 -2.395137 0.0201302

IL-3

Estimate Std. Error df t value Pr(>|t|)
Time 0.3819202 0.1262965 56.47376 3.023997 0.0037486
Survivaldead:Time -0.5153221 0.2164933 55.43845 -2.380315 0.0207621

IFN-a2

Estimate Std. Error df t value Pr(>|t|)
Time 7.368396 2.774815 55.86789 2.655454 0.0103010
AgeGreater60older:Time -7.704730 3.239224 55.41990 -2.378573 0.0208522
AgeGreater60older 44.726614 21.390894 24.55143 2.090918 0.0470524

MCP-1

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT 680.47441 249.43832 27.52168 2.728027 0.0109658
VAD.IndicationDT:Time -91.32862 39.28639 57.36259 -2.324688 0.0236507

num Total PBMC

IL-6

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh:Time -18.54127 8.228838 55.84312 -2.253206 0.0281925

G-CSF

Estimate Std. Error df t value Pr(>|t|)

CD27-38++ transitional

Estimate Std. Error df t value Pr(>|t|)
Time 0.1924463 0.0792502 69.27155 2.428340 0.0177687
AgeGreater60older:Time -0.2343863 0.1043092 67.73029 -2.247034 0.0279003

CD3 of live lymph

Estimate Std. Error df t value Pr(>|t|)
Survivaldead:Time -1.829251 0.8242817 66.33046 -2.219206 0.0298978

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
SexMale -20.7260608 7.6448089 21.84154 -2.711129 0.0128054
Time -0.9006538 0.3895893 66.29166 -2.311803 0.0239044

IL-8

Estimate Std. Error df t value Pr(>|t|)
RVADYes:Time 10.23949 4.619034 54.36592 2.216804 0.030839

CD19+CD5+CD24hi

Estimate Std. Error df t value Pr(>|t|)
Time 0.5935719 0.1694987 65.72053 3.501927 0.0008357
OutcomeAlive s/p OHT:Time -0.5739728 0.1953535 65.49771 -2.938124 0.0045549

Eotaxin

Estimate Std. Error df t value Pr(>|t|)
SexMale 128.54981 47.668530 18.34905 2.696744 0.0145850
Time 11.40686 4.560498 55.36903 2.501231 0.0153620
SexMale:Time -11.77662 5.342803 55.18872 -2.204202 0.0316985

MCP-1

Estimate Std. Error df t value Pr(>|t|)
RVADYes:Time 93.61331 42.84101 54.30507 2.185133 0.0332124

TGF-a

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh:Time -1.1233582 0.5157778 54.51579 -2.177989 0.0337513
Time 0.8378511 0.4175992 54.60020 2.006352 0.0497822

CD3 of live lymph

Estimate Std. Error df t value Pr(>|t|)
SexMale:Time -1.143545 0.5277063 66.36504 -2.167011 0.0338279

TNF-a

Estimate Std. Error df t value Pr(>|t|)

TNF-a

Estimate Std. Error df t value Pr(>|t|)
SexMale:Time 3.767525 1.740926 57.15748 2.164093 0.0346496
Time -3.158273 1.483377 57.67143 -2.129110 0.0375269

IL-15

Estimate Std. Error df t value Pr(>|t|)
Time 0.4821335 0.1913209 54.97323 2.520026 0.0146688
VAD.IndicationDT:Time -0.8073232 0.3733218 54.80952 -2.162540 0.0349572

CD27-38++ transitional

Estimate Std. Error df t value Pr(>|t|)
Time 0.2418057 0.0994538 65.99623 2.431336 0.0177663
SexMale:Time -0.2512294 0.1163638 66.05082 -2.159000 0.0344873

CD27-IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
RVADYes 8.138537 3.426992 21.4197 2.374834 0.0269627

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time -2.726267 1.282933 55.03723 -2.125027 0.038089

Eotaxin

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 118.1149 47.98599 19.32249 2.461445 0.0234005

IP-10

Estimate Std. Error df t value Pr(>|t|)
OutcomeAlive s/p OHT:Time 114.5049 42.55788 53.61146 2.690569 0.009491

lymph

Estimate Std. Error df t value Pr(>|t|)

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Time 0.5382593 0.1602283 67.49846 3.359327 0.0012883
AgeGreater60older:Time -0.4334330 0.2101801 66.78306 -2.062198 0.0430827

GM-CSF

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 34.128884 14.361503 20.92304 2.376415 0.0271092
AgeGreater60older:Time -4.022234 1.970956 54.45762 -2.040753 0.0461351

G-CSF

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 114.60139 42.096120 38.21983 2.722374 0.0097107
AgeGreater60older:Time -16.24051 7.977444 57.78234 -2.035804 0.0463639

CD19CD24hiCD38-memory

Estimate Std. Error df t value Pr(>|t|)

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 2.044292 0.8970807 39.13156 2.278828 0.0282137

CD19+CD11b+

Estimate Std. Error df t value Pr(>|t|)
Time 0.7895884 0.2140589 63.20206 3.688650 0.0004710
OutcomeAlive s/p OHT:Time -0.6983342 0.2465523 63.12486 -2.832397 0.0061934

CD27+38++plasma blasts

Estimate Std. Error df t value Pr(>|t|)
Time 0.1018402 0.0463711 65.98620 2.196200 0.0315970
VAD.IndicationDT:Time -0.1781890 0.0880343 66.32796 -2.024086 0.0469921

Eotaxin

Estimate Std. Error df t value Pr(>|t|)
SexMale 128.54981 47.668530 18.34905 2.696744 0.0145850
Time 11.40686 4.560498 55.36903 2.501231 0.0153620
SexMale:Time -11.77662 5.342803 55.18872 -2.204202 0.0316985

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:Time -4.960664 1.778346 62.05844 -2.789481 0.0070021
OutcomeDied post OHT 42.763036 16.441772 20.60956 2.600878 0.0168391

CD19+CD27+CD24hi

Estimate Std. Error df t value Pr(>|t|)

Fractalkine

Estimate Std. Error df t value Pr(>|t|)
Time 9.181153 4.034219 54.31416 2.275819 0.0268244
LowIntermacsHigh:Time -10.023051 4.986464 54.05128 -2.010052 0.0494276

num lymph

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:Time 5487.098 2740.267 7534.727 2.002396 0.045278

IL-5

Estimate Std. Error df t value Pr(>|t|)

CD268 of +27-38++transitional

Estimate Std. Error df t value Pr(>|t|)

IL-8

Estimate Std. Error df t value Pr(>|t|)

G-CSF

Estimate Std. Error df t value Pr(>|t|)

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
Time -0.5331745 0.2039893 64.53309 -2.613738 0.0111329

TGF-a

Estimate Std. Error df t value Pr(>|t|)

Eotaxin

Estimate Std. Error df t value Pr(>|t|)
Time 9.357596 4.075572 55.21464 2.29602 0.0254948

Two-way repeated measures anova

We analyzed the differences in B-cell levels for various features using a two-way repeated measures ANOVA. Here we report variables that had a statistically significant variance (\(p<0.05\)) across groups, or groups at each timepoint.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(4,5,6,8,9,10,12,13)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(bcellcyto)
bcells <- names(df.lmer)[bcells.ix]

models.b <- mclapply(groupvars, function(this_groupvar){
    models.bcells <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " * factor(Time) + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        #return(this_pvalues)
        return(list(model = this_model,
                    pvals = this_pvalues))
    })
    names(models.bcells) <- colnames(df)[bcellcyto]
    pvalues <- do.call(rbind, lapply(models.bcells, function(x) x$pvals))
    rownames(pvalues) <- bcells
    #return(pvalues)
    return(list(model = models.bcells,
                pvals = pvalues))
}, mc.cores = detectCores()-1)
names(models.b) <- groupvars
pvals <- lapply(models.b, function(x) x$pvals)
# something wrong here
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))



# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]

pvals.df <- melt(pvals.matrix)
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- colnames(pvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.short <- qvals.df[pvals.df$qvalue <= 0.05,]
shortlist <- qvals.df.short[order(qvals.df.short$qvalue),]



kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")
Significant results
B-cell parameter qvalue
791 MCP-1 VAD.Indication:factor(Time) 0.000
893 CD19+CD268+ Outcome:factor(Time) 0.000
885 CD27+IgD+ unswitched memory Outcome:factor(Time) 0.003
530 G-CSF RVAD:factor(Time) 0.022
297 CD19+27+IgD-38++IgG ASC LowIntermacs 0.026
790 IP-10 VAD.Indication:factor(Time) 0.027
2 num lymph AgeGreater60 0.030
90 CD268 of +27-38++transitional AgeGreater60:factor(Time) 0.073
618 CD27+IgD-IgM+ switched memory Sensitized:factor(Time) 0.088
770 TNF-a VAD.Indication:factor(Time) 0.091
858 MCP-1 Outcome 0.183
514 IL-6 RVAD:factor(Time) 0.209
137 lymph Sex 0.227
766 CD19+27+IgD-38++IgG ASC VAD.Indication:factor(Time) 0.244
301 TNF-a LowIntermacs 0.253
498 CD19+27+IgD-38++IgG ASC RVAD:factor(Time) 0.266
46 IL-15 AgeGreater60 0.268
49 IL-8 AgeGreater60 0.295
345 CD27-38++ transitional LowIntermacs:factor(Time) 0.296
3 lymph AgeGreater60 0.300
518 IL-8 RVAD:factor(Time) 0.307
883 CD27+IgD- switched memory Outcome:factor(Time) 0.350
992 MCP-1 Survival 0.416
383 IFN-a2 LowIntermacs:factor(Time) 0.420
765 CD19+CD5+CD11b+ VAD.Indication:factor(Time) 0.424
69 num lymph AgeGreater60:factor(Time) 0.426
215 CD27+IgD+ unswitched memory Sex:factor(Time) 0.466
818 CD27+IgD+ unswitched memory Outcome 0.469
78 CD27-IgD+ mature naive AgeGreater60:factor(Time) 0.487
879 CD19+CD27+ Outcome:factor(Time) 0.527
128 G-CSF AgeGreater60:factor(Time) 0.566
878 CD19+CD27- Outcome:factor(Time) 0.571
271 lymph LowIntermacs 0.578
371 IL-5 LowIntermacs:factor(Time) 0.585
526 MIP-1a RVAD:factor(Time) 0.596
817 CD27-IgD- switched memory Outcome 0.626
900 CD19+27+IgD-38++IgG ASC Outcome:factor(Time) 0.634
739 num lymph VAD.Indication:factor(Time) 0.639
97 IL-12(p40) AgeGreater60:factor(Time) 0.653
886 CD27+IgD-IgM+ switched memory Outcome:factor(Time) 0.658
150 CD27+IgD+IgM+ nonswitched memory Sex 0.662
483 CD27+IgD+ unswitched memory RVAD:factor(Time) 0.667
217 CD27+IgD+IgM+ nonswitched memory Sex:factor(Time) 0.677
115 IFN-a2 AgeGreater60:factor(Time) 0.682
491 CD19+CD268+ RVAD:factor(Time) 0.688
33 TNF-a AgeGreater60 0.706
1027 CD19+CD268+ Survival:factor(Time) 0.717
762 CD19+CD5+ VAD.Indication:factor(Time) 0.719
377 IL-1b LowIntermacs:factor(Time) 0.719
731 G-CSF VAD.Indication 0.724
51 Eotaxin AgeGreater60 0.725
820 CD27+IgD+IgM+ nonswitched memory Outcome 0.730
904 TNF-a Outcome:factor(Time) 0.733
183 IL-8 Sex 0.743
490 CD19+27-38+CD5+transitionals RVAD:factor(Time) 0.745
89 CD19+CD268+ AgeGreater60:factor(Time) 0.745
416 CD27+IgD+ unswitched memory RVAD 0.748
185 Eotaxin Sex 0.755
1062 MIP-1a Survival:factor(Time) 0.764
29 CD19+27+IgD-38++IgG ASC AgeGreater60 0.765
234 TNF-a Sex:factor(Time) 0.766
20 CD19CD24hiCD38-memory AgeGreater60 0.769

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We drew attention to specific features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    this_bcell <- as.character(shortlist$`B-cell`[ii])
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    
    sumtable <- suppressMessages(summary(models.b[[this_group]]$model[[this_bcell]]$model))
    sumtable <- as.data.frame(sumtable$coefficients)[-1, ,drop=F] # drop intercept
    if(!("Pr(>|t|)" %in% colnames(sumtable))) next()
    sigsum <- sumtable[sumtable[,"Pr(>|t|)"] <= 0.05, , drop = F]
    
    print(kable(sigsum[order(sigsum[,"Pr(>|t|)"]),,drop=F], row.names = T) %>%
              kable_styling(bootstrap_options = c("striped", 
                                                  "hover", 
                                                  "condensed",
                                                  "responsive"),
                            font_size = 12)
    )
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

MCP-1

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)1 1396.314 299.8357 49.76712 4.656931 2.42e-05

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:factor(Time)3 -58.61538 14.204316 46.18057 -4.126590 0.0001523
OutcomeDied post OHT:factor(Time)8 -55.76061 13.538518 46.05387 -4.118664 0.0001567
factor(Time)21 -27.32462 8.270658 46.63558 -3.303802 0.0018378
OutcomeAlive s/p OHT:factor(Time)21 27.40774 9.419763 46.53642 2.909600 0.0055342

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:factor(Time)8 -27.59770 4.932075 46.11295 -5.595555 0.0000012
OutcomeDied post OHT 27.31810 7.270274 22.64763 3.757506 0.0010464
OutcomeDied post OHT:factor(Time)1 -13.37738 5.047487 45.94619 -2.650306 0.0109931

G-CSF

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)1 274.3137 66.34657 49.26911 4.134557 0.0001379

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 3.095 1.279721 68.99999 2.418497 0.0182277

IP-10

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)5 -999.2242 294.0807 48.43045 -3.397789 0.0013666
factor(Time)5 463.4742 156.2366 49.13082 2.966490 0.0046413
VAD.IndicationDT:factor(Time)8 -806.2164 291.6175 48.29663 -2.764637 0.0080454
VAD.IndicationDT:factor(Time)3 -723.7126 291.6175 48.29663 -2.481719 0.0166053
factor(Time)8 335.4664 151.5492 48.68098 2.213581 0.0315752

num lymph

CD268 of +27-38++transitional

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older:factor(Time)8 -27.22177 10.33554 54.58102 -2.633803 0.0109632
factor(Time)14 -24.92941 12.43679 55.44562 -2.004489 0.0499111

CD27+IgD-IgM+ switched memory

Estimate Std. Error df t value Pr(>|t|)
SensitizedYes:factor(Time)3 17.965877 5.302290 23.31979 3.388324 0.0024949
factor(Time)5 9.747852 4.547781 23.30374 2.143431 0.0427391

TNF-a

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)3 -52.08619 14.55496 49.73388 -3.578586 0.0007821
VAD.IndicationDT:factor(Time)8 -51.15105 14.55496 49.73388 -3.514337 0.0009497
VAD.IndicationDT:factor(Time)5 -48.24997 14.67386 49.90668 -3.288159 0.0018522
VAD.IndicationDT 39.41501 12.48552 46.96050 3.156857 0.0027848

MCP-1

Estimate Std. Error df t value Pr(>|t|)

IL-6

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)8 303.4749 81.51192 47.97526 3.723074 0.0005171

lymph

Estimate Std. Error df t value Pr(>|t|)
SexMale -22.22807 9.001586 53.16948 -2.46935 0.0167811

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
factor(Time)5 4.722429 1.330913 60.14684 3.548263 0.0007592
VAD.IndicationDT:factor(Time)5 -6.577445 1.874482 57.09610 -3.508940 0.0008850

TNF-a

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 29.20377 11.74819 61.39306 2.48581 0.015663

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)5 11.55470 2.912778 59.01745 3.966900 0.0001997
factor(Time)14 -2.17979 1.043488 56.60859 -2.088945 0.0412201

IL-15

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older 6.953102 3.180869 41.10986 2.185913 0.034572

IL-8

Estimate Std. Error df t value Pr(>|t|)

CD27-38++ transitional

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh:factor(Time)21 -11.380113 2.894440 59.65375 -3.931715 0.0002222
factor(Time)21 9.440964 2.450218 61.33981 3.853112 0.0002815

lymph

Estimate Std. Error df t value Pr(>|t|)

IL-8

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)8 121.59744 36.36762 48.07180 3.343564 0.0016092
factor(Time)1 37.88158 16.80689 49.14302 2.253931 0.0286938

CD27+IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)
factor(Time)14 16.67333 5.808017 46.36242 2.870744 0.0061524
OutcomeDied:factor(Time)14 -23.76894 9.569855 46.52627 -2.483731 0.0166625
OutcomeDied:factor(Time)3 -20.00222 8.099103 47.20672 -2.469683 0.0171942
factor(Time)21 14.08234 6.267358 47.25499 2.246934 0.0293537
OutcomeDied:factor(Time)8 -16.05135 7.162484 46.77082 -2.241032 0.0298109
OutcomeDied:factor(Time)5 -15.19473 6.853678 46.39420 -2.217018 0.0315609
OutcomeAlive s/p OHT:factor(Time)14 -14.36929 6.997197 46.40850 -2.053579 0.0456758

MCP-1

Estimate Std. Error df t value Pr(>|t|)

IFN-a2

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh:factor(Time)5 -83.38244 25.81665 48.86115 -3.229793 0.0022176
LowIntermacsHigh:factor(Time)8 -74.03854 26.08098 49.05338 -2.838794 0.0065708
LowIntermacsHigh:factor(Time)3 -71.00660 26.08098 49.05338 -2.722543 0.0089451
LowIntermacsHigh 64.16333 23.82257 40.13825 2.693384 0.0102726
factor(Time)8 53.76254 21.43059 49.46030 2.508682 0.0154438
factor(Time)5 42.82700 20.30165 48.76539 2.109532 0.0400558

CD19+CD5+CD11b+

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)21 18.60843 5.699112 56.68510 3.265145 0.0018584
VAD.IndicationDT:factor(Time)8 10.32734 4.156816 55.37079 2.484436 0.0160275

num lymph

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
SexMale:factor(Time)8 9.848232 2.746704 54.41204 3.585472 0.0007200
factor(Time)8 -8.159681 2.352215 54.37376 -3.468936 0.0010300
factor(Time)14 -8.101354 3.062814 55.12449 -2.645069 0.0106199
SexMale -10.353852 3.922084 26.14988 -2.639885 0.0138005
factor(Time)5 -7.781354 3.062814 55.12449 -2.540590 0.0139123
SexMale:factor(Time)5 8.682914 3.494502 55.06965 2.484736 0.0160327
SexMale:factor(Time)3 5.898797 2.849382 54.68689 2.070202 0.0431688
SexMale:factor(Time)14 7.448953 3.625289 54.98976 2.054720 0.0446698

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:factor(Time)8 -27.59770 4.932075 46.11295 -5.595555 0.0000012
OutcomeDied post OHT 27.31810 7.270274 22.64763 3.757506 0.0010464
OutcomeDied post OHT:factor(Time)1 -13.37738 5.047487 45.94619 -2.650306 0.0109931

CD27-IgD+ mature naive

Estimate Std. Error df t value Pr(>|t|)
factor(Time)14 -19.34727 6.403688 54.77962 -3.021270 0.0038210
AgeGreater60older:factor(Time)14 17.50253 7.722534 54.66856 2.266424 0.0274049

CD19+CD27+

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:factor(Time)8 -30.68974 11.896519 46.35300 -2.579725 0.0131220
OutcomeDied:factor(Time)8 -18.18883 8.283555 46.91757 -2.195776 0.0330881
OutcomeDied:factor(Time)3 -19.62377 9.360583 47.47076 -2.096426 0.0413996

G-CSF

Estimate Std. Error df t value Pr(>|t|)

CD19+CD27-

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT:factor(Time)8 30.68584 11.990106 46.36468 2.559264 0.0138150
OutcomeDied:factor(Time)8 18.19237 8.347928 46.94556 2.179267 0.0343639
OutcomeDied:factor(Time)3 19.65350 9.432447 47.51498 2.083606 0.0425996

lymph

Estimate Std. Error df t value Pr(>|t|)

IL-5

Estimate Std. Error df t value Pr(>|t|)
LowIntermacsHigh 14.33625 4.540629 50.69022 3.157327 0.0026812
LowIntermacsHigh:factor(Time)5 -16.35500 5.441291 49.36905 -3.005720 0.0041567
LowIntermacsHigh:factor(Time)3 -15.72446 5.494185 49.66232 -2.862019 0.0061466
LowIntermacsHigh:factor(Time)8 -13.08094 5.494185 49.66232 -2.380870 0.0211494
LowIntermacsHigh:factor(Time)1 -12.48229 5.441291 49.36905 -2.293994 0.0260840

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)3 41.28308 12.390641 48.60325 3.331795 0.0016549
factor(Time)3 -11.46708 5.569941 49.22844 -2.058743 0.0448305

CD27-IgD- switched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied:factor(Time)8 19.81291 9.256631 50.10634 2.140402 0.0372133

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied:factor(Time)5 7.770913 2.375060 48.89002 3.271880 0.0019634
OutcomeAlive s/p OHT:factor(Time)5 6.157210 2.440892 50.31147 2.522524 0.0148592
factor(Time)14 -4.926591 2.013314 49.03199 -2.447005 0.0180351
factor(Time)5 -3.476482 1.679134 48.86411 -2.070402 0.0437202

num lymph

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)21 218165.6 69573.59 5501.95 3.135753 0.0017232

IL-12(p40)

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older:factor(Time)3 -39.92823 16.10410 49.75241 -2.479383 0.0165940
AgeGreater60older:factor(Time)8 -39.09737 16.10410 49.75241 -2.427790 0.0188521
AgeGreater60older:factor(Time)5 -36.10684 15.62485 49.43068 -2.310860 0.0250524
factor(Time)8 29.12464 13.88649 50.03739 2.097336 0.0410371

CD27+IgD-IgM+ switched memory

Estimate Std. Error df t value Pr(>|t|)
factor(Time)14 24.15202 6.766597 47.02757 3.569301 0.0008372
factor(Time)5 17.32212 5.643461 47.08561 3.069415 0.0035536
OutcomeAlive s/p OHT:factor(Time)14 -21.39064 8.148405 47.15503 -2.625132 0.0116397
OutcomeDied:factor(Time)14 -24.01191 11.131654 47.47898 -2.157084 0.0360919
OutcomeDied:factor(Time)5 -16.38868 7.982839 47.08336 -2.052989 0.0456533

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
SexMale -31.50752 9.007301 37.48747 -3.497997 0.0012251
SexMale:factor(Time)8 27.02760 8.735159 54.73131 3.094116 0.0031071
SexMale:factor(Time)3 24.44017 9.046112 55.34926 2.701732 0.0091385
factor(Time)8 -20.00903 7.482252 54.65772 -2.674199 0.0098594
factor(Time)3 -15.59653 7.482252 54.65772 -2.084470 0.0418040

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)8 -10.16212 3.121996 55.38520 -3.255008 0.0019363
RVADYes 11.57901 3.999725 34.08235 2.894952 0.0065730

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
SexMale -31.50752 9.007301 37.48747 -3.497997 0.0012251
SexMale:factor(Time)8 27.02760 8.735159 54.73131 3.094116 0.0031071
SexMale:factor(Time)3 24.44017 9.046112 55.34926 2.701732 0.0091385
factor(Time)8 -20.00903 7.482252 54.65772 -2.674199 0.0098594
factor(Time)3 -15.59653 7.482252 54.65772 -2.084470 0.0418040

IFN-a2

Estimate Std. Error df t value Pr(>|t|)
AgeGreater60older:factor(Time)5 -80.13405 27.57557 48.91764 -2.905980 0.0054855
AgeGreater60older 59.42952 25.37722 42.42564 2.341845 0.0239558

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)3 -27.99543 10.480918 56.66595 -2.671086 0.0098514
RVADYes:factor(Time)8 -20.33650 9.017817 55.39965 -2.255146 0.0280950

TNF-a

Estimate Std. Error df t value Pr(>|t|)

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
Survivaldead:factor(Time)8 -21.73092 8.069861 55.34926 -2.692849 0.0093560
Survivaldead:factor(Time)3 -18.76413 8.701780 55.67109 -2.156355 0.0353913

CD19+CD5+

Estimate Std. Error df t value Pr(>|t|)
VAD.IndicationDT:factor(Time)8 13.77699 5.156496 55.63322 2.671773 0.0098785
VAD.IndicationDT:factor(Time)21 18.57915 7.056032 57.34477 2.633087 0.0108560

IL-1b

Estimate Std. Error df t value Pr(>|t|)
factor(Time)8 7.942204 2.258115 47.94441 3.517183 0.0009652
LowIntermacsHigh:factor(Time)8 -8.641904 2.704957 47.76020 -3.194840 0.0024795
LowIntermacsHigh 5.410829 2.448628 37.85728 2.209739 0.0332453

G-CSF

Estimate Std. Error df t value Pr(>|t|)

Eotaxin

Estimate Std. Error df t value Pr(>|t|)

CD27+IgD+IgM+ nonswitched memory

Estimate Std. Error df t value Pr(>|t|)
OutcomeDied post OHT 51.75397 18.81024 34.01848 2.751372 0.0094435
OutcomeDied post OHT:factor(Time)8 -45.26825 18.42339 46.22234 -2.457108 0.0178180

TNF-a

Estimate Std. Error df t value Pr(>|t|)
factor(Time)3 -60.31567 14.86319 41.40883 -4.058057 0.0002141
factor(Time)8 -56.86000 14.86319 41.40883 -3.825558 0.0004327
factor(Time)5 -56.01533 14.86319 41.40883 -3.768729 0.0005127
OutcomeAlive s/p OHT:factor(Time)3 63.98437 17.82884 41.70621 3.588813 0.0008664
OutcomeAlive s/p OHT:factor(Time)5 63.40940 18.10497 42.00334 3.502320 0.0011083
OutcomeAlive s/p OHT -52.98502 15.21731 40.22085 -3.481892 0.0012142
OutcomeDied:factor(Time)8 67.94475 19.66215 41.40883 3.455611 0.0012815
OutcomeDied:factor(Time)3 67.52042 19.66215 41.40883 3.434030 0.0013633
OutcomeAlive s/p OHT:factor(Time)8 60.98559 17.82884 41.70621 3.420615 0.0014097
OutcomeDied -55.94450 16.90936 39.24878 -3.308493 0.0020156
OutcomeDied:factor(Time)5 57.59233 19.66215 41.40883 2.929096 0.0055066
factor(Time)1 -48.29333 16.88835 42.69337 -2.859564 0.0065379
OutcomeAlive s/p OHT:factor(Time)1 48.18291 19.55600 42.67658 2.463842 0.0178540
OutcomeDied post OHT -62.76000 25.56455 39.24878 -2.454962 0.0186237
OutcomeDied:factor(Time)1 48.27333 21.23446 42.22333 2.273348 0.0281535

IL-8

Estimate Std. Error df t value Pr(>|t|)

CD19+27-38+CD5+transitionals

Estimate Std. Error df t value Pr(>|t|)

CD19+CD268+

Estimate Std. Error df t value Pr(>|t|)
factor(Time)14 -34.03871 8.813153 54.50836 -3.862263 0.0003005
AgeGreater60older:factor(Time)14 34.33335 10.627446 54.41277 3.230631 0.0020977
factor(Time)21 -17.19694 6.912037 54.73129 -2.487970 0.0159222
AgeGreater60older:factor(Time)3 19.32183 8.006711 54.60495 2.413204 0.0191972
factor(Time)8 -11.89000 5.511085 53.89410 -2.157470 0.0354451

CD27+IgD+ unswitched memory

Estimate Std. Error df t value Pr(>|t|)
RVADYes:factor(Time)8 -10.16212 3.121996 55.38520 -3.255008 0.0019363
RVADYes 11.57901 3.999725 34.08235 2.894952 0.0065730

Eotaxin

Estimate Std. Error df t value Pr(>|t|)

MIP-1a

Estimate Std. Error df t value Pr(>|t|)
Survivaldead:factor(Time)3 32.83650 10.707160 48.95956 3.066780 0.0035187
factor(Time)3 -14.18310 6.214399 49.55959 -2.282297 0.0268003
Survivaldead:factor(Time)1 21.90146 10.827348 49.22900 2.022790 0.0485437

CD19+27+IgD-38++IgG ASC

Estimate Std. Error df t value Pr(>|t|)

TNF-a

Estimate Std. Error df t value Pr(>|t|)
factor(Time)3 -37.21699 12.78570 50.77795 -2.910831 0.0053410
factor(Time)5 -37.50816 13.62272 52.06253 -2.753353 0.0081034
factor(Time)8 -37.45827 13.62272 52.06253 -2.749690 0.0081827
SexMale:factor(Time)8 41.33081 15.82776 51.47185 2.611286 0.0117898
SexMale:factor(Time)3 39.58387 15.23657 50.60393 2.597952 0.0122521
factor(Time)1 -32.66139 12.78570 50.77795 -2.554526 0.0136757
SexMale:factor(Time)5 39.72646 15.94812 51.60286 2.490980 0.0159951
SexMale -25.81619 12.80567 54.31830 -2.015997 0.0487575

CD19CD24hiCD38-memory

Estimate Std. Error df t value Pr(>|t|)